100 Most Recent Reviews

• yeBZMH1eU457toXtayf1WQ==January 8, 2026fall 2025

  High-Performance Computer Architecture

  I have no background in computer architecture not a computer science degree. I have exposure to OS and have been a professional SWE for 5 years. I found I had to catch up lots of hardware related topics that increased my weekly workload to 20 hours/week. Otherwise, it could be 10-15 hours/week.

  The material is great. Top notch, equal to the CMU course on YouTube. However, and I cannot stress this enough, THE PROJECTS SUCK!

  You spend 5-10 hours tweaking parameters and recording perf. The end result is learning something obvious like "more cores = more overhead" or that "out of order execution order saves time". Really obvious information from the lecture that doesn't need repetition. I would have LOVED to implement some hardcore structures like writing a cache coherence simulation or coding reorder buffer logic.

  The most coding is ~50 lines of code to write LRU cache. This is a basic leetcode question. Not okay. I expected better. The course seriously needs a revamp here. I hope the professor reads this.

  Rating: 3 / 5Difficulty: 4 / 5Workload: 20 hours / week

• kPRbSxTFjIkenr0EYxqjcQ==January 8, 2026fall 2025

  Human-Computer Interaction

  This course has a great professor who has a passion for the subject. Course was organized pretty well. There are two large projects broken up into small parts to make them digestible. The second project is a repeat of the first but in a group setting, which I didn't find valuable. The assignments are all written assignments (no coding).

  Rating: 4 / 5Difficulty: 3 / 5Workload: 11 hours / week

• 7o5/Qgul8567CvjdgPz0Qg==January 8, 2026fall 2025

  AI, Ethics, and Society

  This course is a good introduction to explainability and fairness in ML/AI. It is named the same as a conference called the AAAI/ACM conference on AIES which specializes on the same topic this course teaches. There is also a journal that publishes proceedings of the conference. I mention this because it gives students insight into exactly what you're signing up for. This is not a computer ethics class nor an AI ethics class in the pure sense. The "society" part is important because it takes a sociological lens and fairness here is understood in terms of bias against social groups. So once we understand the purpose of the class the material all makes sense. I docked a star because some of the assignments felt tedious, but I acknowledge it's hard to test for knowledge without some repetitive tasks. This is a highly important subject that is understudied and rarely used in industry but definitely necessary.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 7 hours / week

• 67SytxTnAw+4x2y4ZT4gvg==January 8, 2026spring 2025

  Special Topics: Compilers - Theory and Practice

  This course was by far the most challenging I've taken so far at OMSCS.

  I came in with decent antlr knowledge from work experience .... I can only imagine what someone coming without that felt like.

  The final phase of the project was pretty brutal, I started early but still came down to the end. If you do not start early, you will certainly fail, the amount of hours to design/implement a solution is steep. You also often need to significantly refactor previous iterations of the project to accomplish the next step I found, which ate up a bunch of time.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 25 hours / week

• Fk0FQSMCiLF4JSvmuaGBhg==January 8, 2026fall 2025

  Quantum Computing

  This class was really nice and interesting imo. I suggest it. Two exams, 4 labs, the exams are proctored which is annoying but I get why. Very informative class.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 8 hours / week

• QJfnPoFqMua2oeZuSZNI1g==January 7, 2026fall 2025

  Computer Networks

  I learned quite a lot from this course, mainly from reading the textbook and doing the projects. The lectures are fairly boring for many of them, especially when half of them are just paraphrased passages from the textbook. I strongly recommend reading the textbook, it goes a bit more in-depth and helps you understand all of the required materials. The quizzes are easy as long as you have a good understanding of what you just learned. The assignments aren't that bad, I finished them in half a day each project. The exams are completely fair. The professor is mostly missing the entire semester which is unfortunate but, it is what it is. Overall, good course, but lectures could be improved.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 4 hours / week

• QJfnPoFqMua2oeZuSZNI1g==January 7, 2026fall 2025

  Deep Learning

  This course taught me a lot about Deep Learning. Some of the lectures could be better and the Meta lectures are mostly awful; however, self teaching through other materials such as StatQuest or Stanford lectures are very useful. Do not bother reading the textbook, it is absurdly in-depth and is not useful. The quizzes can be studied for using the study guides the course staff releases, but even then, some of the questions on the quiz just make you go "what???". The projects are very interesting but can be pretty brutal. Good course.

  Rating: 5 / 5Difficulty: 5 / 5Workload: 25 hours / week

• gJxd5E3NulatPmx8Q9V2AA==January 7, 2026spring 2025

  Big Data Analytics for Healthcare

  Great class overall to take near the end of the ML spec. The final project is very well thought out with topics given to us to choose from rather than us cooking up topics.

  The final project is done in duos so its not that bad and it can be as hard as you want it to be. I learnt a lot building and training a model from scratch for the final project.

  The exam is easy if you go through the lectures.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 15 hours / week

• gJxd5E3NulatPmx8Q9V2AA==January 7, 2026spring 2025

  Data and Visual Analytics

  Do not take this class. You will work in a group of 5 to do a project that one person can do in the Ai era.

  Although you will learn a bit about full stack dev and Apis and learning how to learn random things quickly.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 12 hours / week

• gJxd5E3NulatPmx8Q9V2AA==January 7, 2026spring 2025

  Database Systems Concepts and Design

  One of the most useful courses in OMS that you can take. It will take a lot of teamwork to get through the project's early stages so be prepared.

  One of the TAs is great, responds within the hour to any questions you ask.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week

• XWsfAaR0RRg6WjjxRoF0Dg==January 5, 2026fall 2025

  Introduction to Information Security

  Coming into this course its important to understand 1) It is run by TAs 2) it is project based. The other reviews state this as well, so it should not be a surprise. It may definitely have been nice to have the professor more involved, and high-quality lectures related to the course content would have been amazing, but that is not part of the course.

  The projects in the class are structured as a CTF style assignemnt - for the most part, you are working to identify flags hidden in an application. You will need to download a VM for this, so give yourself time to troubleshoot that if needed. The projects are each created and run by teams of 2-4 TAs (easier projects have 2, others have more). There may be more TAs in the background, but I only interacted with 2-4 on ed discussion/office hours. For about 60% of the projects (including the hardest ones), the TA who did the bulk of the work creating the project has long left the position.

  My general gripes with the course -

  1. The TAs are inconsistent - on one project, you may be actively corresponding with the TA who designed and built it, while on others, you are talking to TAs who are more "maintainers."

  2. The quality of TAs - almost none of the TAs actually work in a cybersecurity role professionally - a lot of them are simply folks who took and completed the course previously. Some of them have been a TA for quite some time as well. A recurring theme, though, is that these TAs often have no professional cybersecurity experience, and cannot help/teach further than the projects. Most of these teams no longer have the creator of the project around, so a lot of answers to questions are "just figure it out" or "use your resources." It is pretty clear after the course that a lot of the TA's knowledge of cybersecurity does not go past what is taught in the course and is very surface-level.

  3. Ed discussion moderation - as the 1 - 2 weeks you have for each project goes on, you notice TAs remove/redact less and less from posts (as the volume of posts naturally goes up). If I had just waited until closer to the deadline on a few projects, the answer is pretty clearly written on ed discussion.

  You can find details on individual projects in the other reviews and I generally agree with those. The course heads really need to take a look at the overlap between projects (half of malware analysis just felt like the web exploitation project) and the usefulness of some projects (Machine learning was a complete waste of time). Many of the projects are at least 3-4 years old and may not be as relevant (the log4j project, while interesting, was little more than a wrapper around a hackthebox lab).

  Rating: 2 / 5Difficulty: 1 / 5Workload: 12 hours / week

• oEQOXEftcE6DcQclBrWoWw==January 5, 2026fall 2025

  Distributed Computing

  To be honest, getting B on the course might be easy since getting 62% overall alr gives you that.

  About the latest review prior to mine, i don't think that the last project only costed 15 hours and you alr got 82%. I spend more than 50 hours, code hundreds to a thousand LOC and only got 72%, given that too many edge cases for different parts that were not initially accounting for, hence may need to redo certain parts. I have 4 years of experience in backend development with Spring, solve a thousand of leetcode problem in java, hence my coding ability in java with the Collection framework is not at the beginner level.

  Mid-term and Exam-wise, I think that it depends, you can still study hard but unfortunately get low grade since you may focus on slides, lecture videos while the exam questions focus on the original papers.

  In my opinion, you still can get A if you do the bare minimum for programing parts 3 and 4 which are the main culprits for student depression (5%/5% p0 + 10%/10% p1 + 8%/10% p2 + 5%/15% p3 + 5%/15% p4 = 33%/55%) while getting full marks for participation (5%/5%) and full mark for exam (40%/40%) since cut-off for A is 82%.

  Regarding the contents, i find the first half useful since it makes you reasoning things well and will sharp your mindset into thinking what failure models you may get into, what should be done to prevent that during your system design/technical discussion. The second-half is more about the case studies and does not leave much impression on me.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 30 hours / week

• r9dfALdlHDI51NbnofDZ3A==January 5, 2026fall 2025

  Machine Learning

  The concept of this course is interesting, but the execution is very flawed. Machine Learning focuses on the experimentation portion of machine learning. How do you interpret the results of a model? Is this model appropriate for this dataset? How do you verify a model's "correctness" for larger scale use?

  Unfortunately, the course itself isn't focused on teaching you how to go about doing those things. The reports that make up a bulk of the class work are entirely focused on interpreting and analyzing your experiment results. However, the class has no real introduction/lecture on the metrics that are commonly used or how to appropriately interpret models, leaving us to hopefully stumble into an effective teaching of these on our own.

  As of Fall 2025, we now have 10 - 20 page assignment docs - which provide some guidance into what metrics to look into by virtue of mentioning that a particular plot should be included, but even these aren't complete due to a lack of a formal rubric and additional instructions scattered on Ed. Only after the grades for A1 were out, and sample reports from other students were posted, did I get a better idea of what the "expected" interpretations/metrics were.

  This approach is certainly exploratory, but combined with the grading lottery others have mentioned, the lack of rubrics + scattered/unclear requirements, and the lack of guidance in appropriate exploration of a model... I found myself learning a lot less than I hoped, and instead spent most of my time guessing requirements and formatting LateX papers and plots to fit a strict page limit.

  Rating: 1 / 5Difficulty: 4 / 5Workload: 20 hours / week

• ND4KfHYB+6idCR30DXEWWA==January 5, 2026fall 2025

  Human-Computer Interaction

  Marked the workload at 6 hr/wk but that's on average. There were weeks I spent 10 hours, and weeks I spent less than 3.

  I took this as my first OMSCS course because I heard it was well-structured and a good 'medium' difficulty entry point for graduate level classes. I agree with that. There's a detailed course calendar and all lectures and homework assignments are available at the beginning of the semester. The expectations for students are clearly laid out and I was able to work a week ahead, which helped a lot when stuff in my personal life got busier. I learned a lot and found the subject material super interesting.

  There's a lot of reading, so if you're a slow reader you may want to budget more time. The group project is kind of a waste of time (it's just the exact same thing as the individual project, but in a group), but I found a good group early and we did the project without any issues.

  The way the course is structured, you have to learn everything, do homework, take 4 closed note quizzes, and do a solo project in the first 11 weeks of the class. This is the part of the course that took me 10 hrs/week. Then, the last 5 weeks of the course is only submitting check-ins for the group project and taking 1 (open note) test. Since I had a good group, it took me 3 hours a week max to do the work required for the group project.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 6 hours / week

• GvorwXgJMs6F7hAO5LvZjg==January 4, 2026spring 2025

  Introduction to Information Security

  I achieved a solid A in this class and skipped doing the last assignment.

  Pros: Some fascinating topics. You learn quite a bit about foundational programming and computing topics. Things you are guaranteed to never use in your job, but you feel more accomplished knowing them, and are more well-rounded.

  Cons: If you find solving puzzles completely exciting, and being unable to solve some puzzles frustrating, this course will frustrate you. There were 2-3 assignments I couldn't figure out the final answer to, no matter what I did. I am confident that one of them was likely a bug in the assignment, but the TAs ARE NOT ALLOWED to help you. You either solve it, or you don't.

  I cannot overstate, you need to go into this class expecting that no matter how intelligent or accomplished you are, you may not solve every puzzle; AND be okay with not knowing why. Ha.

  Rating: 3 / 5Difficulty: 4 / 5Workload: 8 hours / week

• GvorwXgJMs6F7hAO5LvZjg==January 4, 2026fall 2025

  Data Analytics and Security

  I achieved a high A in this class.

  Pros: Subject matter and quizzes are straightforward and do not take much time. For half of the semester, you can complete your week's work in about 1-2 hours. The course is heavily weighted toward the final project and paper. If you work on that consistently and early, the course is a breeze. If you have no experience in data analytics, its a great foundational course to get your feet wet.

  Cons: Some of the TAs' grading does not demonstrate competence. I spent around 80 hours on the final project alone because I wanted to really blow it out of the water. Those 80 hours do not count my teammates' contributions. And we received a B on our final project because the TA "significantly" did not follow the grading rubric. I found that quite frustrating and un-academic. I professional brought it to the TA's and the Professor's attention, and received no response. Many people complained about the TAs grading on the mid-semester project, and I can see why.

  Takeaway, I find that this class is overly weighted toward a complex, many-faceted, final project (30 page paper + 20 minute presentation) that is too complex for TAs to grade correctly.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 4 hours / week

• UGUoDfisXh5NauhtIFsIUg==January 4, 2026fall 2025

  Artificial Intelligence

  This is an exceptionally difficult class. For context: I likely have more experience with Python than the average OMSCS student, but less overall CS experience. I took several related classes to prepare me for this one (KBAI, Game AI, AI4R, etc.), and it was still rough. But how difficult you find each assignment greatly depends on your familiarity with each section, and what your natural aptitudes are. Additionally, part of the difficulty comes from the strict plagiarism policy, which limits the external resources you can consult.

  Here's a breakdown of each assignment:

  A1 - A*. ~60 hours if you haven't taken a graduate-level class that teaches A* at a high rigor, ~20-30 with. Expect extra time if you want a 90%+. It took me ~40, and I found it to be the easiest assignment both to conceptualize and to program, despite most students saying that it's by far the hardest. The time investment comes from implementation size rather than conceptual difficulty.

  A2 - Game playing. ~40h if you're not comfortable with recursion and debugging complex recursive algorithms, ~20h with. I had a terrible time with this assignment due to misinterpreting the provided documentation, but otherwise thought it was conceptually straightforward.

  A3 - Bayes nets. If your understanding of graduate-level stats is solid, this assignment can be done in 5-10 hours. Otherwise, you're in for a rough couple weeks. I spent ~30 hours prepping for the assignment, and another ~30 on the assignment itself. This assignment requires a tenth of the coding of assignment 1, but it was far more difficult for me.

  A4 - Machine learning. If you're comfortable with numpy and vectorization, ~20 hours, ~40 without. This was the first assignment I felt that the provided material was not enough to understand the concepts, so I had to do a lot of self study.

  A5 - Gaussian mixture models. This is a very polarizing assignment. The first half requires what I thought was incomprehensibly obtuse numpy broadcasting and spatial reasoning, and it was easily the worst experience I’ve had in any CS class due to a complete mismatch with my aptitudes. The second half is comparatively trivial and doable in an hour or two. If you have strong linear algebra skills and a good working spatial memory, this will likely be a breeze. Otherwise, expect ~50 hours on the first half alone.

  A6 - Hidden Markov models. I didn't do this assignment, but the folks that did said it was on par with the difficulty of assignment #4.

  Difficulty: A5 > A3 >> A4 > A2 > A1 Time taken: A5 > A1 > A3 > A2 > A4

  The midterms and exams are a great way to review and solidify your understanding of the material. However, you aren't really graded on your understanding of the material, but rather on how well you can solve dozens of problems without any mechanical errors. Being off by one decimal after 2 pages of math is worth 0 points, as is taking the right approach but making a minor mistake along the way. In addition, there are several ambiguously-worded questions and later-corrected solutions, and I found it to be a stressful experience. There's a 24 hour challenge period after the exam ends where you can argue your case for why your incorrect answer should be marked as correct. Expect your grade to jump as much as 1-2 letter grades (yes, 10-20%) after regrading. I asked for clarifications on a couple questions but was denied due to exam policies, so I had to guess between two answers, and later learned I chose the wrong ones. But overall, I found the TAs to be pretty generous and forgiving with points.

  Effort expended on exams does not necessarily correlate with a higher grade. Don't beat yourself up if you test poorly, it doesn't correlate with how well you understand the material.

  Lecture quality and usefulness varies. Some lectures were too high level, and others were better grounded in examples. I strongly recommend reading the textbook. I fully read or skimmed ~1000 pages throughout the course.

  I didn't personally find the discord helpful. Due to the plagiarism policy, most students are hesitant to share any tips, so it's better for morale-checking rather than assignment help. I recommend sticking with EdStem. The TAs are responsive and usually helpful, but they're sometimes hesitant to share concrete tips. Office hours are generally one on one, and I recommend joining those if you have questions or need a code review.

  If you're looking to prepare for this class, I strongly recommend these three things: brush up on A*, learn how to debug (setting breakpoints, stepping through the code, etc.), and learn how numpy broadcasting works in 1D, 2D, and 3D. Linear algebra and calculus would help too, but I didn't struggle on those portions.

  And lastly: this review comes off as intimidating, because that's how I felt throughout the whole of the course. Conversely, I know many folks in discord who thought everything was review and never struggled at all. If you put in the time, you can get through this course. Due to varying assignment difficulty, I spent 10-50 hours/week on this class.

  Rating: 3 / 5Difficulty: 5 / 5Workload: 35 hours / week

• vsibVbdFfYHQ84sN6cGhvw==January 2, 2026spring 2025

  Digital Health Equity

  This class has been available to OMSCS students for a while now but has not been on omscentral for some reason so I'll happily write the first review. I'm only now seeing on omscentral but I took this course a few semesters ago so i might be a bit hazy on some of the details.

  Professor Parker is very knowledgeable in her subject field which made all the lectures easy to understand. She records the lectures herself, so they are well made and seem relatively up to date. This is more of a do your research and write papers type of course and is not code heavy at all.

  You are given weekly lectures and readings about health equity. You will mostly learn about disparities within healthcare and how technology can be used to fight those disparities through the use of apps, websites, or other forms of technology. Based on these lectures and readings, you will write reports every few weeks ( i forgot how many, maybe 3 reports) and in between those reports you will develop 2 papers in which you design a prototype for a chosen health equity issue you wish to address.

  Throughout the semester, you will also work in a group on a specific project of your groups choosing. This can involve code but it can also be completely free from it. It depends on how your group wants to tackle it. This project has milestones which will need to be completed in addition to the research and design papers mentioned above which all make for a pretty writing intensive course.

  For our project we made a high-fidelity non-functional application using FIGMA so if designing is your thing, you'll enjoy this course.

  This class feels like a combination of AIES, Ubiquitous computing (only the good parts), and HCI. Overall, I enjoyed this course and would recommend it.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 10 hours / week

• vsibVbdFfYHQ84sN6cGhvw==January 2, 2026fall 2025

  Introduction to Health Informatics

  I actually really enjoyed the lectures for this course which were a highlight for me. Dr. Duke was able to bring together aspects of healthcare with technology rather seamlessly. I have a biology background, so this portion of the course was a welcome surprise.

  As others have said, this course can be divided into 2 halves. Lectures, quizzes, and labs in the first half and a group project in the second half. I enjoyed the lectures so I had no issues with the quizzes. The labs were pretty straightforward and for some of the labs you were completely guided through them by the TA's. Keep in mind though, that for some labs most of the time will be needed in setting up the proper environments which can be a pretty big headache for some people. But overall, I cant say I learned too much from them.

  The group project, as always has a lot to be desired. I was proactive and tried getting a group early on but was still left with teammates who did jack. Throughout the program, group projects have been my biggest gripe and one of my main sources of frustration. I don't understand how some of these people have jobs at FAANG or other giants and end up contributing so little.

  Anyways, I was able to get a high A but to say I learned a lot would be an overstatement. I enjoyed the healthcare aspect of this course way more than the technology aspect so there's some bias on my end. For the first half, I was on Ed discussion constantly but for the second half, I maybe went on once or twice a week.

  The TA's were great, some of the best in the program. They were quick to answer any questions and provide good guidance if needed.

  So the class was better than I thought it was going to be based on previous reviews but still not the best.

  Lectures: 5/5 Labs: 2/5 TAs:5/5 Project: 1/5

  Rating: 3 / 5Difficulty: 2 / 5Workload: 10 hours / week

• sjjStLXYWzrnvOb/j7kxvQ==January 2, 2026fall 2025

  Data Analytics and Security

  Join this course if you

  • want an easy B or C grade
  • Light coursework
  • Just care about passing, not really understanding anything

  Do not join this course if you

  • want a satisfying A
  • want to actually learn anything about cybersecurity
  • Have a low tolerance for bad TAs

  Having said that - here's my experience with this course

  1. THIS IS NOT A CYBERSECURITY COURSE - This is a 100% STATISTICS course. Barring maybe a throwaway module on security concepts, the entire course is about statistics. Linear models, regression models, clustering etc and then implementing those in R. Final project is also pure statistics, finding some patterns and trends in data - zero cybersecurity. As someone with no interest or background in statistics this course was entirely awful - and I ended up just focusing on whatever needs to be done for grades. I didn't learn anything relevant or useful that could apply to any real world cybersecurity scenario. As a policy s

  2. TA's will ruin this course for you - The assignments are vaguely worded, the rubrics are just as vague and the TA's make random judgement calls with no real recourse. Blanket "no regrades" are issued by TAs and the professor is hands-off, blaming students' grades on "low effort" (his words, in an actual Ed post). No reflection whatsoever that an entire class of 100+ students got bad grades and were complaining daily about TA's. Instead he blamed it on low effort. Students lost 5 to 10 points because TAs thought the paper had too many bullet points, or they didn't agree on where periods should be used, or they don't agree with the grammar structure or format of the write-ups etc.

  3. Group project is a luck of the draw - you're paired against randoms for the group project. I had an awful experience with one person who literally did nothing (not even one word contributed to the final reports). The other one refused to hear any input and made the "group" project his personal project.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 6 hours / week

• 2Me+AQwmW5DaTYXjYAnU8g==January 2, 2026fall 2025

  Time Series Analysis

  It is a good class. The instructions are very detailed and the topics are interesting. If there are any improvements, it would be the question for projects can be a lot more clearer. Sometimes I had to input a lot more codes simply because what it asks for.

  Rating: 5 / 5Difficulty: 5 / 5Workload: 15 hours / week

• MeZgjPPmk12++gjERVeq9g==January 2, 2026fall 2025

  Human-Computer Interaction

  This was my first course in OMSCS (Fall '25). I finished with a 96/100.

  I initially came into this course assuming there would be a lot of writing involved, and I was correct. You will write A LOT and as someone who doesn't really like writing papers, the classwork was not very enjoyable for me. With that being said, as long as you follow the instructions on the HW's and projects and base everything off of the course material you will get an A on them, they're just time consuming.

  The four quizzes are the hardest part of the course, but will also force you to learn the most. Each quiz consists of 5 essay questions - each with several different sub-components. They're fully proctored, no notes or outside help - just your memory. You're given two hours to complete them which is tough due to breadth of each question and the depth of your responses. It was also really difficult to digest any of the required reading material that you're quizzed on. Grading can also be somewhat inconsistent from what I experienced.

  The course is front-loaded like other people have mentioned. In Week #9 we had an exam due, a quiz due, and a project check-In due. Once you've made it to this point - the rest is smooth sailing.

  Exams were proctored but open-everything. They are easy if you’re okay with a B on them - which I was.

  The projects (Individual & Group) are structured the same. If you follow all of the instructions and choose a good task/interface to improve you’ll be fine. The group project comes with all of the same drawbacks of any other group project.

  I was able to fulfill all of my class participation points by doing all of the peer reviews and taking some surveys for other classmate's projects.

  In short, the class is not inherently difficult it’s just time-consuming and requires a lot of writing. I found the material very interesting and the lectures very easy to digest. Dr. Joyner is so good at using examples to explain different concepts.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 18 hours / week

• CNFJRTK8LziCE4STdYeT/Q==January 1, 2026fall 2025

  Machine Learning

  This was my last course of OMSCS and the one that I was most worried about from reading the reviews on here. The course has seen major changes that have genuinely made it easier to pass the class. However, I still feel that this course doesn't do an adequate job of giving you concrete knowledge of ML.

  Pros:

  1. Regrade Requests: The course has introduced the ability to correct issues found by graders for half points. So if you receive a 70% on a report and adequately address all issues highlighted by reviewers, then you could receive a maximum of 85%. I personally did not use this mechanic but others seemed to benefit greatly from it.
  2. Quizzes: The quizzes allow for 1 sheet of notes and a calculator. You are also given multiple attempts. The multiple attempts alone made it extremely useful for me as I typically used 1 attempt to get a baseline of my current knowledge and gaps that needed to be filled.
  3. Exam: The course has done away with the mid-term exam due to the workload of the reports to where we only had a final exam. The final exam is different from the quizzes and I'd argue is easier as it focuses more on concepts centered around the algorithms covered in the class.

  Cons:

  1. Hidden and known rubric issues: In recent years, the requirements have become more clearly defined. You are told exactly what libraries you need to use, what plots are required, and given SOME details on how to structure the papers. However, the known requirements were honestly difficult to cover. For example, In A1 they required an absurd amount of plots and specified that they must be visible without zooming in. This forced me to drastically weaken my analysis just to include all plots. The known rubrics also went through MANY iterations and changes as the assignment deadline approached which I found frustrating. In A1 for example, the TAs defined a requirement, removed it, and defined it again in the span of a week. The hidden rubric also still exists and graders are using requirements that are not clearly defined when grading your assignment.
  2. Grading delays: The grading was incredibly delayed and students had no idea whether they should continue or drop the class as grades for A1 did not come out until days before A2 was due (between these two assignments, that's 25% of your grade in limbo while the drop deadline was closing). The course also tells you that every report is iterative from the previous report in terms of the feedback that you receive. So you're expected to address feedback from A1 in A2 and the other reports. This didn't have a major impact for me and I only had to re-write small portions of my A2 report but this definitely affected other students. Additionally, I found it frustrating that regrade requests for A4 (the final assignment) were not available, with the stated reason being that grading delays pushed the timeline to essentially the end of the semester.

  Recommendations:

  1. Reports: I personally got 92%, 99%, 100%, and 93% across all reports. You are given the option of doing extra credit for an additional 10% but with the already defined requirements, I found it difficult to manage the page limit as it is and opted out of doing the EC. My recommendation would be to clearly define the hidden rubric requirements as early as possible so that you do well on A1 and are not compromised on A2 with the grading delays. Ed Discussion and Office Hours were my main source of assessing the requirements. The TA's will not give clear answers and will give suggestions. I would treat those suggestions as requirements for your report. Also, the known rubrics will change over time so keep an eye on Ed Discussion for the latest version of these documents. I also think there is a disconnect on the purpose of these reports that is not adequately covered in the known and hidden requirements that trips up some students. The primary goal of these reports is to analyze the algorithms and their behavior on the provided datasets. The objective is not to maximize performance metrics or aggressively tune hyperparameters, but rather to explain why the algorithms behave as they do, interpret patterns and trends in the results, and connect those observations back to the underlying theory. Finally, I believe that completeness is valued more than accuracy which is unfortunate. I found myself constantly cutting or reducing my analysis to meet all of the requirements which made my analysis very light and surface-level. A4 was EXCELLENT in my opinion as it was light on plots and heavy on analysis.

  Overall, This course is clearly improving and It's clear that they're taking the feedback from previous semesters into consideration. However, I still think there is room for improvement.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 12 hours / week

• Hlbv1xErB9n1pHPQKEPY4Q==December 31, 2025fall 2025

  Special Topics: High-Dimensional Data Analytics

  ISYE 6525 - HDDA - is the first class I've taken at OMSCS that actually feels like what I expected when I enrolled into a master's level program. Lectures are short with no nonesense, and TAs will absolutely not hold your hand to guide you through the assignments. Coming into this class without knowing how to do matrix calculus and without being comfortable in numpy was tough. The first HW asked me to solve a linear regression without the professor explaining what a linear regression was. I was able to come arround and grasp the necessary material, but it's always frustrating when you realize you're paying money to self-learn. And even still, most of the assigned readings went straight over my head.

  There is no single textbook, but snippets of various (non-required) books and papers to read for each module. You'll have one module every 2 weeks for about 1 hour of lectures, 60 pages of text, and 1 HW. HW can be completed in Matlab, R, or Python, or any other language you choose, though example codes generously provided by the professor only come in these 3 languages. The 2 exams are essentially slightly tougher open book HW problems with 1 week less to complete them. Grading is extremely generous, actually insultingly so - I was frequently given full marks despite my code producing the wrong output.

  I found the lectures and the professor's style of teaching disappointing, and it seems most other reviews did as well. 1 hour's worth of lectures per 2 weeks is not enough for more than surface-level knowledge. It always bothers me when I hear a professor say "here's this formula. It can be proven to be accurate but I'm not going to show you how." You'll get a ton of that in this course. Don't expect to see proofs or derivations in the lectures themselves. This problem is compounded by the expectation that you will be using libraries to get your code to work (rather than implementing the algorithms yourself), so it'll be tough to grasp the details of what you're doing. Frustratingly, I sometimes found myself copy pasting the example codes without understanding how they work. Yet this was enough for an A.

  In summary: although the material was very interesting, I can't confidently say that I've learned it.

  Rating: 2 / 5Difficulty: 3 / 5Workload: 7 hours / week

• Hlbv1xErB9n1pHPQKEPY4Q==December 31, 2025fall 2025

  Applied Cryptography

  CS 6260 - AC - is the most enjoyable class I've taken in the OMSCS program so far. Essentially what you'll be doing is taking the role of a hacker and try to break various presented encryption schemes. For the 2 exams and the 5 written assignments you'll be presenting theoretical hacks on paper, and for the 2 coding assignments you'll be designing and implementing hacks in Python. In other words, you will mostly be solving puzzles. So, you need to be comfortable with relying on your intuition and the provided examples - if you're experienced in playing Zachtronics or similar computer games you should be fine.

  The textbook is not required reading, but the professor is extremely clear and rigorous in her explanations, to the point where sometimes I would zone out of the lectures. Grading is overly strict - if you do not use exact language expected or do not fully show your work you will get points deduced, though you can dispute with the TAs. The cutoff for an A is at 80% score to make up for it. The assignments were very helpful in understanding the lecture content.

  I'm not giving 5 stars because the second half of the class (dealing with asymmetric encryption) had a noticeable drop in quality due to getting bogged down in the details of number theory. There was not enough time to cover the topic in depth, in sharp contrast to the first half of the class. Additionally, I wish we spent more time on practical applications and got to see real life examples. Finally, on the same note, the class did not seem to be rigorous enough for a master's level course. For instance, there were no (mandatory) readings assigned and we never had to prove that a scheme was secure (only insecure).

  Those of you who are privay concious be warned - you will have to install Honorlock spyware to take the 12 quizzes and 2 exams! Recommend using a live USB.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 6 hours / week

• zhE+Nal3x1LLOxyCcniiUg==December 31, 2025summer 2025

  Database System Implementation

  • This course felt like a beta course. It is not a graduate level course (I doubt if this can even be at undergrad level)! The content could have included other topics like Logging, Recovery, Transaction Management, Distributed and Cloud databases, Examples for the features described from modern databases but apparently another course is being prepared for these. If you are looking for a solid understanding of relational databases, just read the recommended book "Database System Concepts, 7th edition (https://www.db-book.com/) and may be go through Andy Pavlo’s youtube videos instead of taking this course.

  • As others said, too much time is given for C++ concepts in the lectures, thus depriving of chances to cover other database concepts. C++ must be made a pre-requisite instead. There is a programming assignment just to check C++ concepts, that could have been used for solidifying other database concepts instead.

  • TAs were mostly low key on Ed during the summer semester. They took many days to respond to student enquiries. I took a couple of courses before this, and TAs in this course are the least participating ones. Ed forum did not see much activity as the TAs and Professor didn’t respond to student queries within a few days in general (I would expect TAs to field most of the questions within a day or two). There was a time when all of them went completely silent for 4-5 days!

  • Lecture slides contained many mistakes from the first run of the course and were not corrected in this run too. It seems like not enough attention or effort is spent to improve the online version of this course. If you are coming to this course expecting GIOS type of rigor and discipline, you’ll be disappointed!

  • Exams and exercise sheets were not that challenging. Programming assignments don’t have much guidance via comments. However if you spend like 4-5 hrs per week, you can end up getting an A easily (assuming you have C++ knowledge).

  • This course’s curve is the most lenient (if you scored >= 80% you get an A grade, otherwise a B) of all the courses. 90% of the students got A! If I knew this beforehand, I’d have studied even less for the exams and exercises and I would have spent more time in reading the book.

  Rating: 2 / 5Difficulty: 1 / 5Workload: 5 hours / week

• 6v6NWG6Kl/hPv2eJJuS8gA==December 31, 2025fall 2025

  Introduction to Graduate Algorithms

  I took GA as my last class, but having done that, I would recommend trying to take it a couple of semesters before you intend to graduate as I'm sure that would make it feel way less stressful. The levels of anxiety that GA can generate are really pretty nuts, so please take this advice seriously.

  It sounds strange, but other than the stress, I don’t really have a ton of complaints about GA. Brito and the TAs seem like genuinely nice and caring people, and I can’t really fault them for any of the negative aspects of GA. They seem to have experimented a lot over the years with how the class is run. They have incorporated student feedback, while still keeping the class rigorous and substantial. I believe they are doing their best.

  The exams felt mostly fair, and the grading was even generous at times. The only real issue I had was that it wasn’t always obvious what kinds of mistakes would result in massive point deductions and which would be treated more leniently, so I recommend getting as much clarification as possible about that from the TAs. Generally though, if I finished a test feeling pretty good about how it went, that was reflected in the grades, and if I felt like something went wrong, the grades reflected that too.

  I recommend attempting every single homework and practice problem that is assigned or mentioned in a post by the TAs, including the coding problems, and the extra practice problems. Doing that along with watching Vigoda's lectures and reading DPV should be sufficient to prepare for the exams.

  Before GA, I had previously taken ML4T, RAIT, ML, DL, CN, GIOS, HPC, HPCA, and Netsci, and I earned A’s in all of those classes. GA was my first B, and I missed an A by less than half of 1%. But I’m grateful that I got through it in one try and graduated OMSCS without having to repeat it.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 24 hours / week

• AsXSpPZZ36Buac6bbnSyRA==December 30, 2025fall 2025

  Graduate Introduction to Operating Systems

  First class in the program, mech engineering bachelors from 10 years ago, some coding experience from the past few years of self-teaching. Overall, I learned a ton about how computers and operating systems work! I thought it was super interesting how applications interact with the OS, all the parts that make up an OS, and there was so much I learned about what goes on behind the scenes it made me in awe of computing systems. It felt like more than 17 hours a week. I would recommend starting projects ASAP and try to stay ahead of the schedule provided for the lecture modules. It helps watching the corresponding modules for each project before starting, so you should complete them before the project is even released ideally to avoid concentrating lots of those hours in heavy weeks.

  Projects - 100, 100, 110 Midterm - 77 Final - 93

  Final grade - A Hours / week - 17

  Projects Directly related to some of the chapters conceptually but you’re pretty much on your own to learn the coding. The time below includes the ReadMe write-up which I spent 8-12 hours on for each project.

  Project 1 - 70 hours. It took a ton of time for me since the only experience I had in C was from cs50 Harvard a long time ago. The warm up parts did a nice job of easing you into the main parts of the project though. It involved using sockets to send and receive data, then implementing parts of a provided library, then making the library multithreaded.

  Project 3 - 40 hours. It was the easiest by a lot in my opinion. Inter-process communication between cache server and proxy to serve requests to clients.

  Project 4 - 60 hours. This was the most rewarding but perhaps the most frustrating. You need to implement a distributed file system using gRPC. It took a really long time to understand all of the steps clearly but it was cool seeing it working and keeping all the files across all clients and the server in sync. I probably spend an extra 10 hours doing the extra credit.

  Tests The midterm was shorter and didnt include as much info as the final. I thought that both were pretty fair; there was a balance of facts and memorization with some questions where you had to apply your knowledge. I probably put in 15 hours for the midterm, and 30 for the final as I went through all my notes thoroughly, did a ton of practice calculations, made and practiced flashcards, etc.

  Lecture Modules (17 total) The lectures are really good and I only had to seek supplemental knowledge / clarification a few times over the course of hundreds of 1-5 minute lecture videos. I took thorough notes and spent and average of 3-5 hours on each module.

  Good The amount of knowledge you will take away from this course. I didnt know a lot about computers in general before this course, so I went off on some learning tangents in the middle of the videos. Overall I feel like I have a much better base than before. Slack was awesome to work through problems with other students and get help, but I did not like Piazza as I hate their UI and found it very hard to use and find what I needed. TAs were really helpful and their attitude in answering questions was proportional to the intelligence of the question. Despite all the hidden stuff lots of people including me complains about (see below), I thought the projects were really well put together and were super captivating.

  Bad In project 1 a little, and project 4, it did feel like we were herded to implement the solution in a specific way. It did take a lot of extra time to figure out the abstracted functions / files in project 1, and in project 4 it felt limiting only being able to edit and submit certain files. However, after spending some time with the code it becomes clear why it is structured this way and in the end I dont have any major qualms with it. The exams are weighted too heavily in my opinion. A project takes ~50 hours and is worth 15%, where exams are 25%. Some exam questions test your memory on little details, so if you lose 5 or 10 points on one of them, it is almost equivalent to 5 hours of project time. I get that the concepts are important and we should know them thoroughly but I would be more happy with the exams and projects being equal, especially since the projects are so grueling and time-intensive. There probably wouldnt need to be such a large curve this way. There are a bunch of errors in the quizzes and some lecture videos, I would have expected them to re-make the videos at some point in the past 10 years instead of leaving errata notes below which are very easy to miss.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 17 hours / week

• ymZc+tY36sXteFxpQeZ28Q==December 30, 2025fall 2025

  Distributed Computing

  I took this course in Fall 2025, and it covered topics I’m genuinely passionate about in distributed systems. I finished the course with an A (88%). Overview The course starts relatively slowly with lectures, followed by Project 0 (intro), Project 1 (client–server), and Project 2 (primary–backup replication). The lecture material is good, and both the required and optional research papers are excellent (long term). Many are still highly relevant in industry today (saved and reading them now after the course :) ). Working knowledge of Java is needed to do well IMO as a good amount of coding/debugging is needed. The real challenge begins with Project 3 (Paxos) and Project 4 (KV Store). IMO, Paxos is the harder of the two. I worked on the projects almost every day, and while demanding, the experience was rewarding. Running tests locally (macos) helps with time and quicker iteration. That said, there are some notable downsides: The DSLabs framework has a steep learning curve. Even by the end of the course, I didn’t feel I fully understood all of its internal workings. Some tests are extremely strict, especially with tight timeouts and DSLabs-specific requirements (e.g., running with --checks for idempotency, checking correct equals() / hashCode() behavior for search tests, etc.). It’s worth noting that passing all tests is not required, and the grading curve helps significantly.

  Pros A wealth of high-quality material, especially valuable if you already have experience building software components from the ground up. Project4 reference implementation helps and didn't find it confusing. Well-structured lectures with a great selection of research papers. Exams are easier than AOS, fully objective and with sufficient time. The trade-off: each question carries more weight, so mistakes are costlier.

  Cons The test framework for Projects 3 and 4 is rigorous. Must start early and invest time understanding readme.md and DSLabs framework.

  Final Thoughts Overall, this is a strong and rewarding course for students interested in distributed systems, especially those who enjoy both theory and hands-on implementation. If you’re willing to commit time consistently and don’t get discouraged by test failures, you’ll learn a lot.

  That said, I think the course could benefit from leaning more clearly toward either learning (theory) or implementation, rather than straddling both as heavily as it currently does.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 35 hours / week

• uGdPLFYuNjNE0R+60hFMKg==December 30, 2025fall 2025

  Statistical Modeling and Regression Analysis

  This class is overbloated garbage. Everything feels like a chore. They shoved 2 midterms, both coding and mc into it and the instructions for the coding portion is a multistep mess. There's a crappy group project but you cannot even pick a topic you want to do. The least offensive portion of the course is the homework, but even that is overly long. Some of the TAs are also very condescending and feel the need to add extra bs to their responses to student's questions. Just take another elective over this trash class.

  Rating: 1 / 5Difficulty: 3 / 5Workload: 10 hours / week

• toohORXUybDr8ej6qtCbDA==December 30, 2025fall 2025

  Introduction to Information Security

  This course felt more like a bunch of mini-courses, with most modules having a different set of TAs. It's entirely CTF based now. Most projects had flags that you'd submit, and the tasks that required code submissions ran it through an autograder. There's no subjective grading: you know your grade after each submission. There were some assignments with submission limits, but they were lenient and I never felt much pressure.

  This course will tear you up if you don't have a decent coding background. I took two years of Python in undergrad and still had to take some time brushing up on things.

  Some modules were better than others. MITM was easy and made for a good warmup. Malware analysis consisted almost entirely of reading reports and was kind of a letdown. Binary exploitation was tough for a lot of us (including me) but it was very informative.

  Extra credit was available on some assignments. You can't depend on it being there, but they do offer it at least sometimes. I'd say about 3 extra points in total were available on your final grade if you went for all of it.

  The professor was entirely absent. Not an announcement post, not a lecture video, nothing. The class was entirely run by TAs, at least from my perspective. That said, the TAs were helpful and the discussion boards will be your friend. You'll need to know how to do outside research (follow the syllabus) if something isn't familiar to you.

  The workload varies a lot. MITM took me ~10 hours or less, whereas binary exploitation probably took me at least 40.

  VM setup worked just fine on my Windows laptop. You'll want to know basic Linux commands (how to navigate the filesystem and run scripts, for example).

  I'd suggest doing some practice on HTB Academy ($8 a month with your student email) and/or picoCTF (free). There's other resources out there as well, like DVWA if you want to practice Database Security before you start the course.

  Overall, it was difficult but not unbearable. I got an A while working full time. I passed the OSCP in 2024 which helped a bit but there was still a lot of learning I had to do.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 25 hours / week

• tQDYQZAdQEOyu+fkV4eVtw==December 29, 2025fall 2025

  Human-Computer Interaction

  TLDR: While I finished the course with a high A, I had mixed feelings about this class. In particular, I felt like there was a significant imbalance between the course's workload (somewhat high) and the actual depth and difficulty of the work (pretty low), which I feel led to me disengaging somewhat with the material; I found myself wishing topics were explored in much more depth. I did not feel like the team project actually was able to dive deep enough into the design process to justify its existence.

  This course is pretty evenly divided into thirds. For the first 1/3rd of the course (the "Content" phase), you are watching the entirety of the class's lecture content, along with completing a weekly homework assignment (Homeworks are 4x5% = 20% final grade). The course content is divided into two major units, Principles (essentially the theories behind interface design) and Methods (the design lifecycle, how to prototype and evaluate an interface). The lectures are excellent, Dr. Joyner is an exceptional presenter and brings very good energy to the videos, they are very easy watches and convey the necessary information very well. I found the actual content behind the "Principles" unit to be much more interesting than the "Methods" unit, which often felt a bit surface-level (I'm a grad student, I know what different types of data are and how to use statistical tests), but the course endeavors to be entry-level, so whatever. The homeworks each consisted of answering 4 questions in 8 or fewer pages. Like many things in this course, it felt like these were graded quite easily and straightforwardly.

  The second third of the course (the "Practice" phase) felt like it ramped up the workload quite a bit. This phase consisted of reading the associated texts with the course, completing four quizes (4x5% = 20% final grade), along with working on the individual project (15% final grade), and taking 1 out of the 2 course tests (10% final grade). The readings varied in quality considerably. Many of them felt like "slightly-reworded-lecture-content-but-worse", but a few of them were interesting. The quizzes felt very fair. Studying the lecture content felt straightforward (like I said, the lectures were good). One question on each quiz was from the assigned reading, and the instructor informed us ahead of time which reading would be on the quiz (I dunno about this one, this feels maybe a little too nice). The project consisted of selecting a design task, performing needfinding for that task (for 95% of people, this meant posting a survey for the class), designing three prototypes, evaluating these prototypes (for 95% of people, this meant posting a survey for the class), making a higher-fidelity final prototype, and having people evaluate it (for 95% of people, this meant posting a survey for the class). I didn't really mind the project, although it appeared to be a massive procrastination trap for a lot of people. It did sometimes feel like a lot of the survey responses were low-effort (we got participation points for completing surveys). Writing the project report (max 25 pages) felt like the same straightforward grading as the homeworks; if you do everything the assignment asks, you can expect a 100, no surprises.

  The final third of the course (the "Application" phase), consisted of a team project (15% final grade), along with taking the remaining test (10% final grade). If that sounds a lot easier than the last phase... yep. The tests were open-note, open-internet. They felt like the kinda assignment where its extremely easy to get an 85%, easy to get a 90%, and quite difficult to get a 100% on (which, coincidentally, is the opposite of the rest of the course). I don't have much more to say about them. The team project was nearly an exact reboot of the individual project; the major differences were an increased page limit on the report (max 40 pages), and the vague direction that our prototypes should be higher fidelity. I didn't like this. It didn't really feel like we had a chance to dive deeper into the design process, since (as mentioned), I felt like poor survey responses were kinda bottlenecking the interface design anyway (the instructor plans to require interviews for the team project feedback in the future, which I do think is a good idea). In addition, there is also the classic team-project roulette; my team had one person completely unresponsive, and another who had to be prodded quite a bit to do work. I think these kinds of projects are fundamentally unfair, end of story.

  The remaining 10% of the grade comes from course participation, for most people, this meant spamming low-quality survey responses. I wish there was a better way to align incentives to encourage thoughtful responses, but I can't really think of anything.

  Dr. Joyner intended this course to take ~10 hours per week. I think it mostly does, with the caveat that unless you're willing to work a bit ahead, you will definitely have weeks that exceed that, and that the final third of the course is much easier than the other two. I'd probably scooch a quiz or two into this final phase if I were running the show, but it's not a huge deal.

  I would advise anyone taking this course to heed the instructor's advice about proactively forming your own team instead of doing the matchmaking survey (to increase your odds of avoiding slackers), and to be proactive with the project work. But the class is very straightforward (potentially to a fault) if you don't mind things being somewhat introductory, and having a moderately bumpy workload.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 10 hours / week

• Leq5tv8IK8tghRyJLzn6eA==December 27, 2025fall 2025

  Special Topics: Applied Natural Language Processing

  This was by far the worst course I took on the programme. Don't expect to learn much from the classes (as they have nothing but a teacher reading the slides without taking note of anything) - most of the things I learned came from putting the class transcripts into ChatGPT to get explanations. The assignments are disappointing as well… they were useful in a certain way because I had to learn how to work locally using .py files (in a "object-oriented way"), but I found them pretty easy (as much of the code is provided). Overall, I learnt some stuff (and the course content is pretty up to date), but it wasn't worth my time or money.

  Rating: 2 / 5Difficulty: 2 / 5Workload: 7 hours / week

• BbZ3VI+UXIBBvTaYgCBzpw==December 24, 2025summer 2025

  Introduction to Cyber-Physical Systems Security

  This is for Fall 2025. I really enjoyed CS-6263 and found the projects fun…but that could be due to my background in logic and circuit design. I echo the same advice others have given — start the projects early.

  Project 1 took about 25 hours total and consisted of a part A and B. Part A only took about 5 hours, including reading up on how to use the tool and watching tutorials. Part B was more challenging but in a fun way — I actually completed it in 15 hours but spent another 5 hours fine-tuning things.

  Project 2 took about 15 hours. Ladder Logic is a breeze if you’re familiar with logic gates and parallel signal propagation. If not, then it could take 2-3x longer to finish the project while you brush up on related concepts.

  Project 3 was different from past semesters and only took about 5 hours to practice sniffing for ICS devices.

  Project 4 gave brief exposure to machine learning. It seemed daunting at first but turned out easier than I expected. It helps if you’re familiar with Python or object-oriented programming. You are provided with a working skeleton model and have to fill in additional portions of code to make the project functional. Once you get your ML model to run properly, then you further refine and optimize your machine learning algorithm to hit the grading targets for accuracy and relevancy.

  I was a little worried at the beginning of the semester after reading some of the other reviews because the difficulty level can be deceiving. Fortunately, my background in computer engineering served as a great foundation for all four projects and I finished each project with anywhere from 5-9 days to spare (primarily because I started projects early). I actually struggled more with the exams.

  It is true, the lectures are completely independent of the projects. I didn’t mind this as I could focus more on the projects early on and come back to the lecture material later when it came closer to the midterm/final. You are allowed one page single-sided notes for the midterm and one page double-sided for the final. The lectures are all done well, some of the best production values I’ve seen in OMSCS so far, and I found the material all very interesting. Still, I was thrown off by the wording of some exam questions and received a C on the midterm and a B on the final. I still finished with an A overall in the course by averaging 98% across all projects.

  There is also a 20-minute video presentation you have to do based on a research article. It is an easy 100 points. You get to select your presentation date and I quickly picked the latest available week for due dates. I suggest you do the same so the presentation doesn’t interfere with your first or second project. By the time you’re on project 3 and 4, the pace of the course really slows down and you’re in a lull in the semester so it’s a perfect period to then focus on your presentation.

  I recommend this class for any of the OMS specializations, either as a requirement or elective, because the course is interesting, the projects are neat and fun, and the difficulty level/time commitment is very manageable for balancing family and career.

  One last remark, I have to give credit to the amazing TA support team. The TAs ran Ed Discussion superbly and were responsive and gave helpful advice. Office hours were also extremely helpful. I was kind of shocked at how few people attend office hours; sometimes it was just me and the TA one-on-one (which was great for getting personalized help). Maybe everybody else knew what they were doing and didn’t need the help but I was grateful for how accessible the TAs were. Whenever I was in a quandary, or found the project instructions unclear, it was quick and easy to get clarity in office hours or through the discussion board.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week

• cvUKRHrSDa+Z2dn5TUe48Q==December 24, 2025fall 2025

  Database System Implementation

  Medium-effort, high ROI course. 4 Assignments 3 quizzes 2 exams

  Great class if you are interested in understanding how databases are built from scratch. The assignments focus on implementing a beta version of BuzzDB in C++ and iteratively improving it over the course of the assignments. One of the nicer things is that there are no hidden test-cases. Passing the test-cases locally almost always guarantees a 100% on gradescope. B+ tree (assignment 3) was the most challenging one.

  Quizzes: These are proctored quizzes (via honorlock) that mainly focus on the lectures. Combined make up around 25% of the grade. They aren't too bad , but there are 3-5 questions that can seem from material not included in the lectures but need general database knowledge to answer

  Exams: Same as the quizzes but with the material from the papers, especially the final was mostly from material in the papers. But the professor mentions the important sections from the papers relevant to the exam. My advice would be to focus on those.

  Curve 85% was an A , it looks like the course is getting tougher with time. % of As has decreased compared to previous semesters.

  One of my criticisms is the weightage of assignments relative to quizzes, quizzes seem like beta exams, and sometimes I felt that they weren't really needed . Instead they could add up-to 2 extra assignments dealing with in-depth concepts from databases. This is where the course missed the bar slightly.

  Overall , a great course, put in the effort and you will be fine. The professor and TAs genuinely care and want the students to succeed. My grade A - (89%)

  Rating: 5 / 5Difficulty: 3 / 5Workload: 14 hours / week

• 2c1btKEcjhtxtHHB8f2rMg==December 24, 2025fall 2025

  Knowledge-Based AI

  Background: 1st semester student, Undergrad CS major, working as a Data Scientist for less than a year.

  Finished the course a month early and ended up with an A(91.89%).

  Pros:

  • The lectures are very interesting and enjoyable, and they are presented in a way that’s easy to understand.
  • The coding assignments aren’t too difficult and are manageable.
  • The TA and Dr. Joyner are great and respond quickly when help is needed.
  • The integration with gradescope is cool, you are able to submit coding assignments up to 40 times and it auto grades so you can see your grade right away. Cons:
  • The lectures don’t align very closely with the assignments; the assignments aren’t really AI focused and feel more like general coding tasks.
  • The writing assignments feel somewhat useless and repetitive more like busywork that could be avoided.
  • I received feedback on early assignments, but later in the course I didn’t get any feedback at all.

  Homework(15% )- 90/100: These are writing assignments (journals) based primarily on lecture material. Make sure to follow the prompt questions closely and answer them clearly and completely. Demonstrate a strong understanding of the relevant lecture concepts and apply them accurately. When a prompt asks for a diagram, ensure that it matches the one shown in the lecture exactly. In a previous assignment, I mixed up two concepts and received no credit for either, which resulted in a C on that assignment so accuracy is very important.

  Exams: Midterm(7.5%) - 70.91/100, Final(7.5%) - 92.72/100: I’m not a great test taker, so I didn’t do very well on the exams. They aren’t too hard since they are open notes and open internet including AI, but you still need to understand the concepts really well. On the first exam I struggled with time management and ended up turning to ChatGPT as a last resort, which didn’t help much. After that poor result, I made really good notes and used NotebookLM by Google to ask questions based on them for the second exam, and I ended up doing much better. I recommend that if you are unsure if a statement is correct to leave it unselected.

  Mini Projects: Performance(15%) - 98.5/100, Journals(15%) - 95.4/100: The coding assignments are fairly easy and similar to medium-level LeetCode problems, so they shouldn’t take too long, except for Mini-Project 2 (Block World), which was difficult and cost me a few points. Overall, the assignments are manageable. The writing assignments are short (max 4 pages), but I lost points for not including metrics related to efficiency and performance.

  ARC-AGI project: Performance(7.5%) - 100/100, Journals(7.5%) - 100/100: For the assignments, you’ll be solving ARC-AGI problems by creating an agent. I didn’t implement any real AI methods myself, what I did was design a specific solution approach for each problem. To achieve full credit on each milestone, you need to pass at least 6 out of 16 general test cases and 6 out of 16 hidden test cases. The journals can be repetitive, so feel free to reuse the same structure each time. Just make sure you include specific metrics for each milestone, including: Efficiency: Big-O complexity and actual runtime, Performance: Number of test cases passed Keep in mind that even though you only need 6/16 for both general and hidden tests at each milestone, you’ll eventually need to solve all of them for the final project. Because of that, I recommend completing as many problems as possible throughout the milestones rather than waiting until the end.

  Final ARC-AGI project: Performance(7.5%) - 84.38/100, Journals(7.5%) - 76/100: Each milestone (Milestones B through D) includes 16 general tests and 16 hidden tests, for a total of 48 general tests and 48 hidden tests across all milestones. I wasn’t able to solve 15 of the hidden tests, which left me with a performance score of 81/96. My performance wasn’t as strong as it could’ve been because I decided it wasn’t worthwhile to spend hours trying to figure out each hidden test for just a 1 point increase. The final journal grading is much stricter than the other journals. I answered every question on the assignment, but some parts were considered vague by the TAs, which lowered my score. I put in the same level of effort as I did for every milestone, where I earned 100 out of 100, but the TAs expected more for the final journal. I recommend being very clear and keeping in mind that the grading is stricter.

  Participation(10%) - 100 / 100: These points are basically free, and there are plenty of opportunities to earn them. I mainly completed peer reviews each week to stay ahead. After about two months, I had already earned the full 90/90 points and didn’t have to think about it for the rest of the semester.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week

• UxM4W8UQJZ4uBeXkBIQvBQ==December 23, 2025fall 2025

  Introduction to Graduate Algorithms

  This was my least favorite course so far although I do understand the importance. Many of the algorithms are not practical but help establish a mindset on how to simplify.

  Students will learn how to assess the time complexity of an algorithm, some common approaches to simplifying and solving problems and how to deal with problems that may not have clear solutions. This I liked. I also liked the lectures, although they were all recycled from a former instructor.

  The homework assignments are graded but have zero weight on the final grade. Exams have the heaviest weight. The multiple choice problems are tricky and the grading is subjective and probably dependent on who graded your work and how they were feeling when they did it. It was hard to review exam results. You are not given direct feedback on multiple choice questions and for the proofs the feedback is obscure. I didn’t really care to try to interpret it once I had a B.

  There is minimal coding in the course. Most of the coursework involves solving problems in words, similar to writing proofs.

  There is a textbook for the course, Algorithms by Dasgupta. This and the recorded lectures are assigned in a disjointed order throughout the course, which disrupts the flow. The textbook is also concise and often requires the reader to fill in some ideas themselves, which required me to do a lot of rereading

  In conclusion, I found that the course useful, I mostly disliked the work (quizzes, exams and homework), the grading was harsh and subjective and the textbook was difficult to follow.

  Rating: 3 / 5Difficulty: 4 / 5Workload: 8 hours / week

• 4v+GPNibbZHV4cDoKVlvvg==December 23, 2025fall 2025

  Video Game Design and Programming

  As others have said, watch the lectures at 1.5-2x speed. The content in them is good though, there's a lot of really interesting stuff. Unfortunately I do think a lot of references that he makes will be lost on folks who aren't into gaming. There's a LOT of "history of gaming" style stuff, which makes sense when studying game design, but is much easier to grasp if you are into gaming and so have some context as to who Valve or id Software are.

  The project is what you make of it, but is easily the biggest time sink in the course. Coordinating with teammates is hard in Unity (merge conflicts are basically unsolvable, so you have to work on different things), so a lot of cognitive overhead comes from just that aspect of the project. As for actually learning Unity, yeah, you could just do it yourself - but Dr. Wilson gives some assignments that help introduce you to the engine, which can be intimidating. And I really do believe there's some value in working on a game with other people in a team to see how they do things, even if there is a lot of overhead in the coordination. Also, being forced to go through the process of making a game from start to finish, with all of the steps in between (alpha, playtesting, etc) is really a good exercise in forcing you to do steps you might want to ignore if you're just playing around on your own.

  All in all, not a hard class, but you can really go wild with the project if you want, which could end up taking a lot of time. Take this class if you want a semi-structured excuse to play around with Unity for several weeks/months, and if you want some interesting discussion of game design philosophy.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 20 hours / week

• Flq5Ybni4B0gY/9Ddy8jjQ==December 23, 2025fall 2025

  Graduate Introduction to Operating Systems

  This is my first OMSCS course and I feel I learned quite a bit. I did not formally study Computer Science in my undergrad so everything over here was pretty much new to me or things I didn't know in a lot of detail. I really enjoyed the projects and I feel that was where I learned the most. The mid term was relatively easy but that could also be because the first two modules are easier than the last two. The effort I put into the course was quite sporadic, to be honest. I mostly spent the weekend before project deadlines working on the project. This is not how I like to do things but I just joined a new team at work and there was a lot going on so unfortunately this was the case. I would recommend getting headstart on the projects because there were really times when I was so stressed and worried that I wouldn't be able to complete the project but I was lucky and did well on the projects. The end term was the hardest for me and I barely got 48 hours to prep for it but that was because of my personal circumstances. However, because I did well on mid term and the projects I still managed to get an A. Overall, I feel if you keep up with the schedule they recommend, you can comfortably do the course. You get plenty of time between projects as well. The exams are MCQ and fill in the blank style but similar to the quizzes in the lecture. The end term is relatively harder and I would recommend allocating good chunk of time for that. The professor and TAs were all very nice and gave pretty good feedback as well.

  Rating: 4 / 5Difficulty: 2 / 5Workload: 5 hours / week

• lVaErvC+H+S2COrzPeOalw==December 23, 2025fall 2025

  Introduction to Health Informatics

  Finished the course with an A, achieving a 102.88 %

  Background: Bachelor's degree in Computer Science from a university ranked #350-400 out of 436 National Universities in U.S. News English is my second language (TOEFL score: 95/120) 1 year of experience as a full-stack developer and 6 months of experience in data analytics

  Overall: This class is fairly easy, especially if you have experience with full-stack development or have built applications before. If you’re interested in working in the healthcare industry, this course provides an entry-level introduction to using REST APIs and interacting with a testing server. You can be as creative as you want with the project and even try out new tech stacks you’ve never used before. As long as you put in reasonable effort and build something enjoyable, getting a good grade is very achievable.

  Labs (39.07 / 35%): There are six labs plus two extra credit labs, and as long as you follow the instructions, you should get full credit. Some students on the forum complained that the setup instructions didn’t work in their environment. If you’re going to graduate from OMSCS, you should understand that no single setup works perfectly for everyone. Read the error messages and debug your environment yourself.

  Quizzes (19.31 / 20%): The quiz questions mainly test whether you watched the lectures. They’re pretty challenging, but you get two attempts, so overall it’s fair. I’m a horrible test taker(Check Digital Marketing Review).

  Practicum Project (34.5/ 35%): You can form your own group, and finding teammates who match your working style is ideal. It’s pretty easy to earn a high score, and the workload can feel either very light or very heavy depending on your group dynamics. Overall, the points are easy to earn.

  Other (6 / 6%): These are survey points—easy to earn.

  Participation (5 / 5%): These points are also easy. Try to complete them early so you don’t have to worry about them later in the semester.

  Rating: 4 / 5Difficulty: 1 / 5Workload: 6 hours / week

• lVaErvC+H+S2COrzPeOalw==December 23, 2025fall 2025

  Digital Marketing

  Finished the course with an B, achieving a 83.71 %

  Background: Bachelor's degree in Computer Science from a university ranked #350-400 out of 436 National Universities in U.S. News English is my second language (TOEFL score: 95/120) 1 year of experience as a full-stack developer and 6 months of experience in data analytics

  Overall: This is probably the only B I got in the OMSCS program. I got As in my other courses like GA, ML, AI, KBAI, etc. I took this class because I felt a bit burned-out and wanted an easier semester, but it turned out harder than I thought based on my background. The biggest challenge was the exams because they focused too much on remembering English words and terms. Since English is not my first language, the questions were long and hard to understand sometimes, even though I studied.

  Still, I don’t regret taking this class. It was well organized and helped me learn how people use computers and mobile devices. As a data analyst who builds machine learning models for medical systems, this class gave me good ideas about how to collect and use user data.

  Major-Case Reflection Assignments (20 / 20%): These assignments are interesting, and as long as you understand what the question is asking and answer it correctly, it shouldn’t be a problem to get 100s.

  Weekly Mini-Case Discussions (20 / 20%): Smaller than Major-Case, you only need to watch the lecture video and explain what you think about the question. It’s fun and helps you learn some real historical cases.

  Midterm Exam & Final Exam (22.28 + 21.43 / 60%): The weight for this is just too high, as the overall summary explains. I tried my best.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 6 hours / week

• FC25WM4yyLYkJyZFjWz6mg==December 22, 2025fall 2025

  Data and Visual Analytics

  I’m genuinely surprised this a required course for the OMSA program. If Georgia Tech prides themselves on providing leading education, this course is certainly not living up to that standard.

  The content feels like a hodgepodge of loosely connected material, nominally centered on visualization, but without much depth or cohesion. The lectures are extremely broad and don’t align well with the assignments. The workload is lighter than in most other OMSA courses. The D3 assignment in Homework 2 is the only part that takes a bit longer. D3 is tedious to learn and I highly recommend completing D3.js Essential Training by Emma Sanders on LinkedIn Learning. It will significantly help in completing the assignments.

  Half of the course grade is based on a group project with at least five team members. I generally dislike group projects with people I don’t know, but ironically, this ended up being the most enjoyable part of the class.

  Advice for Taking This Course:

  • Take it later in your program. You’ll be better equipped to define a meaningful project once you have more context from other OMSA or OMSCS classes.
  • Form a mixed group. A team with both OMSA and OMSCS students will bring strengths from the analytical and the computer science areas.
  • Choose a manageable project. Pick something with a clearly defined goal and a dataset that can be obtained. Start with a simple idea - you can always add complexity later.
  • Find your group early. Be proactive. Try forming your group at the very start of the course (or even just before it begins).
  • Meet weekly and stay organized. Set an agenda, assign roles and tasks, and ensure at least one team member keeps the project on track.
  • Check the checklists! The course, and especially the project, is full of detailed “to-do” lists that can make you feel micro-managed to the n-th degree! If you want a good grade, be sure to complete every item on those lists.

  Rating: 1 / 5Difficulty: 2 / 5Workload: 10 hours / week

• /Uu79+lLvUsTQmFQj8joqA==December 22, 2025fall 2025

  Applied Cryptography

  My Impressions of the Course

  This was my first course in OMSCS. I made a decent A.

  This review may be a slight outlier because my academic background influenced how I approached this course (I have a master's degree in mathematics). If you don't have a mathematics background, it can be a bit difficult to adjust to how this course flows, because it's essentially a mathematics course in the formalism.

  Good Points

  • The TAs were very helpful and prompt to answer questions.
    • I even got answers to questions outside of the scope of the course (for instance, a proof regarding the DES complement property).
  • The material was presented in a very understandable way with a references given in the weekly Ed posts for reading from the recommended texts.
  • The homework assignments were very interesting, for the most part (coding homework 2 being the outlier; I'll touch on that later).
    • Coding Homework 1, in particular, was a very nice balance of being possible to break without requiring too much knowledge to break.
  • The quizzes were a bit on the easy side, but at least kept me engaged and accountable to some sort of routine.
  • The exams were just the right level of difficulty, where the points I lost I could entirely blame on myself.

  Neutral Points

  • There was no discussion on the designs of any of the cryptographic primitives or schemes presented.
    • I understand that this course isn't about implementation and all of the associated quirks (like constant-time operations to avoid side-channel attacks, which are meddled with by compiler optimizations), but at least having an idea of what the tools do would have been interesting.
    • I would have been okay with references to the standards / specifications.
  • While readings were given from the texts for the material, I would have liked if there were more readings from the literature to go into deeper detail or to see additional aspects that might not have been considered.
    • "Communication Theory of Secrecy Systems" by Shannon, "New Directions in Cryptography" by Diffie and Hellman, "A Method for Obtaining Digital Signatures and Public-Key Cryptosystems" by Rivest, Shamir, and Adleman, and many more papers are absolutely foundational and should be known by anyone in cryptography.
    • And papers like "Mining Ps and Qs: Detecting Weak Keys in Network Devices" by Heninger, Durumeric, Wustrow, and Halderman are important for understanding that weaknesses aren't just about the schemes and models, but about how they're implemented and used.
      • There were a couple of lectures about scheme implementation issues, so I won't say that this concept was completely ignored.
      • Again, however, more references means more opportunities to learn!

  Bad Points

  • If I had to nitpick, I think the worst part of the course was how slow the lectures felt.
    • However, this was easily dealt with by watching at 1.5x speed.
  • Another issue is that I wish it was easier to download the videos for offline viewing.
    • I had to use the developer console to monitor for .m3u8 and .mp4 files and use FFmpeg to download the .m3u8 files to .mp4.
      • Some videos were .m3u8, others .mp4, so I couldn't just search for .m3u8 or just .mp4.
    • FFmpeg command used: ffmpeg -protocol_whitelist file,http,https,tcp,tls,crypto -i video.m3u8 -c copy -bsf:a aac_adtstoasc video.mp4 (here, video.m3u8 is whatever you saved it as)
    • Since each lecture was broken up into 15-20 3-10-minute videos, this was quite tedious.
  • Coding Homework 2 was a bit too trivial.
    • Unfortunately, this comes with the territory.
      • Anything "breakable" is either trivially so or requires more knowledge than this course can give in a single semester from the level that students are expected to enter at.
      • The one exception I can think of was Coding Homework 1's problem.
    • Instead of requiring students to break a scheme, giving students a chance to build something might be a good alternative.
      • Maybe using this as an opportunity to walk students through how to implement a secure scheme using publicly available Python 3 libraries would be better, since it's a very small portion of a student's final grade.

  General Tips/Information

  • You'll want to be comfortable with reading and writing proofs, analyzing what a statement and proof are telling you mathematically, and have a decent grasp of discrete mathematics (elementary number theory, basic combinatorics, discrete probability).
    • If you have time before starting the semester, check out MIT 6.042J (Math for Computer Science).
      • This has basically all of the mathematics you'll need.
  • Pay close attention to definitions. Remember them, internalize them.
    • This means taking the time to understand both the formal and conceptual meanings.
  • Use the proofs in the videos as templates for how you should write your solutions in the written homework and exam problems.
  • I highly recommend that you learn LaTeX so you can typeset notes and solutions.
    • Exams will require typing your solutions in some way, so being able to render your solutions to PDF using LaTeX will be helpful for readability.
    • You'll want to have a local installation for rendering your LaTeX to PDF, because Overleaf (or any other web-based editor) are not allowed during exams.
      • I used VS Code with texlive in WSL2 on Windows 11 and it worked just fine.
  • The lowest written homework grade and lowest quiz grade are dropped at the end.
    • Note that coding homework and exam grades are not dropped.
  • The grades are weighed as follows:
    • Quizzes: 15%
    • Written Homework: 15%
    • Coding Homework: 5%
    • Midterm: 30%
    • Final: 35%
  • There is a curve applied at the end, but the default grade intervals given in the syllabus are:
    • 80% < Grade <= 100% is A
    • 60% < Grade <= 80% is B.
    • So on for each 20% interval below.

  Homework Tips/Information

  • Most of the homework problems were about breaking a proposed scheme or (rarer) building a secure scheme using cryptographic primitives.
    • For the problems that ask you to build a secure scheme, you're not generally expected to prove that they're secure from base principles.
      • Instead, you just cite the relevant results from the lectures.
  • Make sure to pay attention to the resources your attacks use.
  • For written homework, make sure to use Gradescope's labeling feature to label which pages correspond to which problems.
    • You'll lose points if you don't mark the pages!
  • Written homeworks are assigned every other week with one-week deadlines; there are a total of two coding homeworks, each with two-week deadlines.
  • The coding homeworks used Python 3 and were actually very easy.
    • The first coding homework took about an hour of coding and writing the report, but I had the advantage of having implemented the cryptographic tool used in the problem in C++ prior to taking the course.
      • A couple of hours of sitting with the RFC and realizing there's only a small part of the specification that's actually the crux of the weakness of the scheme should be enough to figure it out.
    • The second coding homework was absolutely trivial. It took about 15 minutes, and that includes running the code.
  • My overall homework grades: ~99% for written, 100% for coding.

  Quiz Tips/Information

  • The quizzes are very short and easy.
    • The answers are either verbatim lines from the videos / slides or can be easily deduced in one step.
  • You do need to pay attention to the questions.
    • If you're like me, you may misread a question and answer it incorrectly as a result.
  • For the first half of the course (through the midterm), quizzes test material from the previous week.
  • For the second half of the course (after the midterm), quizzes test material from that week.
    • Don't let the switch up catch you off guard.
      • I think this should have been better communicated (or should have remained consistent).
  • My overall quiz grade: ~97%.

  Exam Tips/Information

  • All exams are open notes but closed internet (besides a few links they whitelist).
    • I suggest having everything they allow you to use downloaded and ready to go on your computer (slides, the main texts, and external notes they may provide), even though they whitelist the links.
      • I felt much more comfortable knowing that I wouldn't have to open extra tabs in my browser and wait for them to load.
  • Use the quizzes as study material for the multiple choice portions of the exams, and use the written homework as study material for the written portions.
    • The multiple choice questions felt like they could have been alternative quiz problems.
    • The written portion of the exams tended to require a bit less insight than the written homework problems in the sense that it was much easier to see what you needed to do to solve them.
  • Don't rush, there's more than enough time (2 hour time limit).
    • Most of the points I lost in this class were on the exams because I didn't use the time as wisely as I should have.
    • My main issue was writing my general idea of an attack but forgetting to return to the problem to fill in the details after moving on.
      • Don't make this mistake; reread your solutions thoroughly before submitting.
  • Just like with written homework, you have to label which pages correspond to which written problem from the exam when uploading to Gradescope.
  • My exam grades: ~92% for midterm, ~86% for final.

  My overall grade:

  • 92% (raw score from the grade calculator provided by the staff)
  • A (letter grade)

  Rating: 5 / 5Difficulty: 2 / 5Workload: 7 hours / week

• y95bmX+tFh3XCTGPOgZ78A==December 22, 2025fall 2025

  Reinforcement Learning and Decision Making

  RL is my 6th course at GT (AI/AI4R/ML/CV/RL). I got an A. I felt as though the course had the following issues:

  • The readings are pulled from a free online book and quite long, covering several topics that aren't pertinent to the projects. Likewise the required lectures are not useful for the projects beyond week 2-3; most students recommend watching a different set of lectures (David Silver). Personally, I didn't find the supplemental lectures or office hours useful. I ended up skipping the readings and lectures after the first few weeks.

  • The environments take a long time to run (e.g. >3 hours); this means you have relatively few attempts to modify hyper-parameters which is challenging because there can be 1-2 dozen parameters to tinker with (and there is no working set of parameters supplied).

  • There is little to no 'hands-on' explanatory material. For example, there is little to no guidance on what kinds of diagnostics to use when trying to find a working set of hyper-parameters, and no discussion of how to interpret the results of those diagnostic measures. A short (time-lapsed) example where the presenter walks through finding a working set of hyper-parameters by examining some set of diagnostics on a problem they aren't already familiar with would have been informative.

  • As with ML & DL, the feedback comes late (6-8 weeks into the program), is infrequent (4-5 week periods), last-minute (prior report's feedback arrives 1-2 days before the deadline of the next project), and is often very brief, e.g. ~30-50 words per 8-page report (RL). I have never lost points for being incorrect (although I likely am), but only for omitting required material. The requirements documents are lengthy (>7 pages) and the (graded) requirements are not always condensed into the 'requirements' section.

  • The final environment is not well-constructed; it's a 90s-looking game that runs at 10 FPS on modern hardware, and only on Intel CPUs. If you have a non-intel processor you will need to rent a computer. The school's PACE cluster may be available, but seeing as the project's assigned at the end of the semester (and around Thanksgiving in the fall), you'll likely be competing heavily with other students for access.

  • The final (MCMA) had little to do with the projects which dominated the time I spent on this course. I got a little above average, which was a bit below 50%. If you're dying to do well, review the (later) lectures and skip the textbook.

  I know more about RL after taking the course, but apart from the credential your time may be better spent doing personal research; the David Silver lectures are free, as is the Sutton & Barto textbook. I liked the OpenAI 'Spinning Up' explanations (e.g. for Proximal Policy Optimization [PPO]).

  Rating: 1 / 5Difficulty: 4 / 5Workload: 24 hours / week

• Jlk0Gmfu1LnHpUt+1kQVyQ==December 22, 2025fall 2025

  Graduate Introduction to Operating Systems

  Background: I graduated in Fall 2024 with a BS in CS at a similarly ranked school. I took Operating Systems there but had to drop the course. This is my first OMSCS course and I am currently work in Devops/Infra. This is the only course I took this semester. I got an A in the course.

  Overall, this was a great course, and a great introduction to OMSCS. The lecture content was interesting and presented in an easy-to-understand method. The lectures were split into 20 or so short videos and some quizzes to accompany them. This made it easy to rewatch parts of the lecture content I had trouble understanding, as I just needed to find the accompanying video. The material itself was not too difficult to understand, however my background definitely helped me in this regard. The readings were also interesting, and lecture content goes over their important concepts in good detail.

  The projects were the real fun portion of the course. I enjoyed every project and learned a great deal from each. They are not OS-specific projects, but deal with network programming, and helped me understand some of the concepts we covered in lecture(synchronization, IPC, gRPC). Projects 1 and 3 were in C, and 4 was in C++. I was comfortable with both so it was never an issue for me, but if you aren’t familiar, it might be difficult to pick up quickly. Overall I probably spent around 40 hours per project, and got full credit on each of them. Make sure to read Piazza posts and the Slack channel for tips, and don’t be scared to ask questions! The test cases weren’t particularly difficult to pass, and you have plenty of submissions. Any test case that fails gives you nice error messages that help you understand where you went wrong.

  The exams were mostly multiple-choice with some matching. I found that they were heavily based on memorization, and the projects don’t really help with the exams. Make sure to understand the lectures thoroughly and use lecture notes provided online to fill in any gaps. Be sure to ask in Piazza or Slack as well. Overall, I found the exams relatively easy.

  In general, this class helped me learn so much more about what the role of an operating system is, and made me a far better programmer regarding managing dynamic memory and multi-threaded applications. I learned so many things in this class! It is hard to put in words just how much I learned. If you like low-level programming and want to learn about the intricacies of the machines we take for granted, take this course!

  Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week

• Qhteq7WngCJs5IrUJuNRwg==December 22, 2025fall 2025

  Database Systems Concepts and Design

  This was my first course at OMSCS, and I chose to take it despite the not-so-great reviews. I was pleasantly surprised by the experience. For context, I work as a data specialist at a small institution and come from what I would call a flawed CS education, so even though I completed a CS bachelor's degree, I consider myself much closer to a career switcher than a traditional CS student.

  The class is well organized with a clear grading breakdown:

  Four exams, each worth 12.5% (50% total) A three-phase final project worth approximately 35% Participation worth 15%

  Course Components

  Lectures: The lectures are pretty good and cover everything you need to know about database systems fundamentals: Relational Algebra and Calculus, Normalization, SQL basics, ER design, Relational Mapping, etc.. They do a good job of preparing you for the exams. There are also methodology lectures that clearly explain what's expected for each phase of the course project.

  Exams: The exams were fair, though attention to detail is VERY important (I can't stress this enough). Please read each question carefully (multiple times if necessary). Practice exams are invaluable for preparation. If you can complete the practice exam without looking up the answers, you should perform well on the actual exam.

  Project: This is the most variable component of the course, as your experience will depend heavily on your group. I was fortunate to have an amazing team, and everything went smoothly, making the workload quite manageable. However, I can easily see how a difficult group dynamic could make the project extremely challenging. Be proactive early in the semester to find a strong group. While you can't predict who might drop, aim to form a team with diverse skills. Ideally, someone experienced with databases (both conceptual design and SQL), someone with backend expertise, and someone with frontend skills. Yes, frontend skills. The Project is a full-stack web application.

  Grading Expectations This course is generally considered an easy B but a challenging A. There appears to be no curve. I scored in the 90s on everything and earned an A (94%). If you struggle on even one exam (and the average on the final was in the 70s) an A becomes difficult to achieve. Keep this in mind before registering; I'm providing this as helpful information, not to discourage you.

  Final Thoughts Overall, I think this is an excellent course. I highly recommend it if you've never taken a database class, love databases, or want to solidify your understanding of database fundamentals. However, this course may not be ideal if you're seeking advanced database topics, looking for an easy A, or prefer to avoid group projects.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week

• PwTXmUqnoUMXfduKETMS/A==December 22, 2025fall 2025

  Machine Learning for Trading

  My background: first semester in OMSCS, CS undergrad, and 5 yoe as a software engineer. No ML or AI knowledge prior to this course.

  This course was awesome. The lectures were interesting, the projects were well-defined and fun to work on. I would recommend everyone take this course. It's the perfect intro to ML concepts.

  My only tip is to make sure to start assignments early, especially the final 2 projects.

  This course does use a lot of Python and Numpy. I have no professional experience in Python and had to re-learn a lot of it. Really not a big deal if you know how to code in any other language.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week

• PwTXmUqnoUMXfduKETMS/A==December 22, 2025fall 2025

  Human-Computer Interaction

  My background: first semester in OMSCS, CS undergrad, and 5 yoe as a software engineer.

  This class was a great intro into HCI and the OMSCS program. I am not the best writer so the amount of written assignments almost scared me away. The assignments have clear directions and even as a weak writer you are basically guaranteed an A as long as you clearly follow the instructions.

  The group and individual projects are basically the same: practicing the design lifecycle using strategies that are well-covered in the course material and writing A LOT about it. Make sure to follow the directions closely and include everything they ask for.

  Overall, group projects put you at the mercy of your teammates. Don't leave your group up to chance: pick your team early to find proactive students to secure your A. I finished the class with an A but the team project almost sent me down to a B as most of my teammates didn't follow the directions and one didn't do anything.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week

• FuW7Lf2BVGTKYArYj7f7ew==December 22, 2025fall 2025

  Artificial Intelligence

  This class provides a broad overview and introduction to topics in Artificial Intelligence. Because of the broad nature of the class, most topics to not build on what came earlier in the class. The first half of the class covered more traditional applications of artificial intelligence, such as search, game playing, and constraint satisfaction. The second half of the class covered probability, Baysian networks, and deep learning. The last time I took a probability course was many, many years ago and my knowledge was not adequate for the second part of the class and resulted in a frustrating experience. The class was well run and the TAs were active in the forum. While I didn’t enjoy the class, I did learn a lot and developed a better understanding of the AI buzzwords that are popular now.

  Rating: 3 / 5Difficulty: 4 / 5Workload: 15 hours / week

• FuW7Lf2BVGTKYArYj7f7ew==December 22, 2025summer 2025

  Introduction to Information Security

  There was nothing to work on for the first week of class. I made use of the extra time by learning how to use Wireshark more effectively. If you can learn how to filter and use the tool, it will help a lot with the Man in the Middle project. This class had no exams or quizzes; it was all projects. The hardest part of this class is the constant pressure of a new project due every week, especially if you are not familiar with the topic that week. Compared to classes with more traditional programming projects it can be frustrating. There often isn’t the ability to make incremental progress, you either get the solution or you don’t. Overall, I enjoyed the class. If taking in the Fall or Spring the relatively light workload would pair well with another class.

  Rating: 3 / 5Difficulty: 2 / 5Workload: 12 hours / week

• sO8OJlQ/P8sVDM5eftGHRA==December 22, 2025fall 2025

  Network Security

  tl;dr - Take IIS and skip this course. IIS is a much better course overall. NS is simply not worth your time or money.

  I have a lot to say about this course, but I'd rather keep this short and just hit the highlights. I'll start with the good and say that the TA's are great and helpful. Truly the one good thing about this course. Second, the malware section of this course is better IMO than the one in IIS. I think they should scrap NS, use the malware section from NS for IIS, and call it a day.

  I felt NS was a colossal waste of time. I only had to spend about 5 to 6 hours each week to get an A. The material is ancient by cybersecurity standards and references material that was published before I even started working in the industry. This isn't to say that information can't still be correct if it's old, but technology has changed so much since a lot of this course was written that I frequently struggled to find the relevancy.

  The projects are a mix between okay and outright terrible. I think the biggest letdown was the ML project. The IIS ML project did feel a little forced into the course, but at least you learned about encodings, training and testing, and fundamentals on how ML works. In this course you get to add some python to a tool that the professor helped author back in 2006! That's it. That's your ML project.

  My two cents is this: do not take this course. GATech should seriously consider either reworking this course or scrapping it altogether. It's a waste of students effort and money to have something in a graduate program that I think barely passes as undergraduate.

  I highly recommend Advanced Topics in Malware Analysis if that's your thing. Otherwise, just take something else, anything else. AIES was better.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 5 hours / week

• ipe1i/snfsp+HDoZP/0IFw==December 20, 2025summer 2025

  Deterministic Optimization

  Taken Fall 2025

  The reason I am leaving a review is because I saw a recent negative post about this class and wanted to chime in.

  Firstly, as someone who enjoys math, I really enjoyed this class. I think the class is well-structured and the homework reinforces the materials since it forces you to apply course concepts. You learn to how to model problems as optimization problems and then methods for solving said problems. It was interesting and felt very practical. I didn’t think the material was hard, but it definitely assumes some familiarity with derivatives and linear algebra.

  Secondly, I’m not sure if there was a curve at all. My assumption was that there was no curve. If there was one, then it was not mentioned at all by staff. I managed to get an A without any curve. Getting an A is definitely doable, but it is stressful because of how heavily weighted each exam is. I firmly believe that if you did the knowledge checks and practice exams, like really really studied them, then you’d be well-prepared for the exams.

  Lastly, the material is definitely relevant. Linear programming might not be prominent in everyday life, but some concepts definitely appear frequently. If your job is to model problems and solve them, then this course is relevant. If you’re in the ML space, then you’re bound to encounter optimization problems since the goal is to either minimize error or maximize performance. See Andrew Ng’s lectures on regression/classification and you’ll see the importance of optimization.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 8 hours / week

• mWlYV8RYUV9ANY+ovth3FQ==December 20, 2025summer 2025

  Reinforcement Learning and Decision Making

  Actually took course in Fall 25, but that option wasn't listed on form.

  Giving the course a 1 because the same feedback is given by almost every student and no attempt has been made to fix it. My feedback is pretty much the same as every other student. Lectures are old and disjoint from projects. No need for the final to be as hard as it is and very taxonomic/unrelated to projects. Ultimately not much better than just seeking out and doing RL projects yourself. For a grade I got near perfect scores on the first three projects and then phoned in the last project and the final. That was enough to get a B, which is all I needed to get reimbursed from my company for the course. Median on the final was ~48%.

  Rating: 1 / 5Difficulty: 5 / 5Workload: 25 hours / week

• KU1E6SWndsiNMKo6qjvF0w==December 20, 2025summer 2025

  Deterministic Optimization

  This course sucks in the way that it forces curve. Only x % got A no matter what. I don’t agree with some comments here praising the course design. It’s pretty much all about linear programming and old school stuff and to be honest, it has almost 0 value in real practice.

  Rating: 1 / 5Difficulty: 2 / 5Workload: 5 hours / week

• u3nsnLbC8JcfIhZUgs8zlg==December 19, 2025summer 2025

  Introduction to Health Informatics

  Fall 2025

  First half of the course with labs, quizzes and lecture videos were great. My average time spent was about 5 hours/week

  Second half of the course with the group project was a disaster because my teammates were not familiar with git and web development plus not willing to spend the time to learn them. Ended up doing the group project myself entirely. I heard that other people enjoyed their group project because they had amazing teammates. Your group project experience really depends on your teammates.

  The course would have been a lot better if I had better teammates. Doing the course project as a solo was prohibited. Choose your teammates wisely. Pick those who have actually web dev/full-stack experience. No amount of gatech courses taken would be a sufficient substitute of that experience. I have heard horror stories of group projects in gatech previously. This time I am the survivor who experienced it first hand.

  Rating: 5 / 5Difficulty: 1 / 5Workload: 20 hours / week

• Y//78ivuAYK34qoqqIUJsA==December 18, 2025summer 2025

  Introduction to Computer Vision

  Actual term: Fall 2025 Final grade: A

  I'm a CS undergraduate and even though I'm not exactly new to computer vision (as I've already taken Deep Learning, and learnt a bit of geometric vision a few months ago) I'm far from actually knowlegeable in the subject.

  My overall experience was positive. There is a ton of material and a ton of work since the assignments require knowledge about the algorithms, implementing them from scratch, and lots of tuning to make them work, at least for grading. Assignment 3 in particular was the most demanding and lengthy, and it was the only assignment where we were given a 2-day extension. I felt kinda burnt out after this assignment and "slacked" in the next two (I did everything except the extra credit sections).

  The final exam was comprehensive but fairly reasonable in difficulty. It's open everything (except for direct answers, especially from LLMs) and multiple-choice. It is actually just an excuse to review the material (and they say so).

  I cannot comment on TAs or the instructor as I never needed them. Everything was covered in the lectures (with an additional independent reasoning), READMEs and FAQs. I don't understand people complaining about unclear expectations, rubrics, etc. They tell you everything you need to know, and expect you to study just a bit for yourself (as should be expected). The only parts where I was not given credit where the sections I didn't answer, and the only parts where I was given partial credit were sections that I intentionally didn't want to answer more precisely (especially those requiring microscopic fine-tuning).

  I did Action Recognition for the final project and it felt like an additional problem set (and it actually was an extra problem set many years ago) with an extra workload since you have to create your own (numeric) dataset out of some videos, but the algorithm itself is really straightforward.

  I think that this course is only a must-take course if you're really serious about computer vision and think that deep methods can't solve everything.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 18 hours / week

• LrS4aqhyfWY1FWNImRt0sw==December 18, 2025fall 2024

  Graduate Introduction to Operating Systems

  Non-CS STEM major (Electrical Engineering), no coding experience in the past 5+ years. Took GIOS as the first class in the program. This was a tough class that consumed a lot of time, especially because I had to learn C, C++, and gRPC on the fly. However, this class teaches you a lot about how operating systems work and makes you a strong (or stronger) C programmer by the end. I would highly recommend this class if you are willing to learn and are prepared to put in the time. Project 1 (Pr1): I spent the most time on this project because I had to learn how to code in C and read Beej’s Guide to Network Programming. This project is broken into four parts. Each part is designed to help you learn what is needed for the subsequent part, and by the end, you can create a multi-threaded getfile library. I spent a significant amount of time compared to others, likely because I had no prior C experience and hadn’t been coding in recent years. • Part 1: 10 hours • Part 2: 10 hours • Part 3: 80 hours • Part 4: 80 hours

  ---

  Project 3 (Pr3): For this project, I spent a lot of time thinking about the design, referring to comments on Slack to understand how other students were approaching the problem, and asking questions to verify my understanding of the project and my design. Writing the actual code was less time-consuming compared to Project 1. • Part 1: 20 hours – This part was simple and helped me get familiar with the CURL library. • Part 2: o 20 hours – Researching and coming up with the design. o 40 hours – Writing code, implementing, and debugging.

  ---

  Project 4 (Pr4): This was the hardest project for me, even though many others thought it was the easiest. It may have been easier for students who already had exposure to gRPC or something similar. If you have time after finishing Project 3 and before starting Project 4, I highly recommend spending some time learning about gRPC—it will help a lot. • Part 1: o 20 hours – Learning how gRPC works and understanding examples. o 10 hours – Writing the actual code. • Part 2: o 40 hours – Figuring out how the overall library was intended to work based on the provided codebase. While I had to do this for all the projects, this one was particularly hard because it was complex, and there were so many different .ch files to read and fully understand before I could grasp how the DFS was intended to work. o 30 hours – Writing the actual code.

  ---

  Midterm Exam: The midterm wasn’t too hard. Just keep up with the lectures and make sure you thoroughly understand the practice exam.

  ---

  Final Exam: The final exam was much more challenging. The amount of material covered on the final is roughly twice what’s covered on the midterm. By the time I finished Project 4, I was six lectures behind, and I had only about 10 days to prepare for the final. Trying to cram all the material into a short amount of time, like a week, was brutal. Since I calculated that I only needed 55+ points to achieve 84% or higher overall, I took the exam without adequate preparation—and that was a big mistake. I spent several days anxiously waiting for the final exam grade because I thought I had bombed the test.

  ---

  Final Thoughts: I agree with the strategy of starting projects as soon as they are posted. As long as you put in the time, refer to the Slack channel for clues when you get stuck, and ask questions, you should be able to score 100% on all the projects. If you get 100% on all projects and score at least the median (~75%) on the exams, you should be able to earn an A, which typically has a cutoff of 81%-84%, depending on the class average. If I had to retake the class, I would allocate 1 hour per day to studying or staying up to date on lectures, even while working on projects. Cramming everything before the midterm and final because I dedicated all my energy to getting perfect scores on Gradescope was not a smart idea. Since the three projects account for 45% of the total grade and the two exams account for 50%, allocating time to exam preparation has a better ROI for achieving a good grade.

  ---

  My Results: I ended up with a grade well above the curve for an A (82% for Fall 2025). • Projects: Full scores (~100%) on all three. The class median was also nearly 100% for all projects. • Midterm: Scored in the 75th percentile, well above the median. • Final: Scored in the 50th percentile, around the median.

  ---

  Rating: 5 / 5Difficulty: 4 / 5Workload: 25 hours / week

• ZduXZVe+NcBIRDua2dy92A==December 18, 2025summer 2025

  Machine Learning

  This review is for Fall 2025 term.

  Just finished the class with an A. It has been a tough class, probably the most laborious and time-consuming among all I’ve taken so far (see below). One piece of advice to those who are going to take this class: have a LOT of time on hands to spend on this class. No matter what your background is, if you have time to spend on this class you are likely to succeed. Also, when you write your reports, it helps to have a dedicated “Hypotheses” section with a few numbered hypotheses that are drawn from the course material or papers (explicitly sighted) and then in the Results/Conclusion sections you explicitly accept or reject each of these numbered hypotheses.

  Background: 7th class in the program (after AI4R, KBAI, ML4T, AI, DL and CN). The material in ML4T, AI and DL was very useful as a pre-courser for ML - most items in this course looked familiar which helped a lot. Prior to that – no/minimal CS but some STEM academic background (from about 20 years ago). I do have some experience with academic writing and LaTeX, both the previous academic background and from current work. Other than that, demanding full-time on-site job, family and other obligations. Taking the program mostly for self-development, not for career change (although – who knows nowadays). I value the courses I take as the ratio of how much new I learned vs. the amount of time and stress it took. For this class, the denominator goes to infinity whereas the numerator, while large, is finite. Hence, it was not my favorite class (so far, the best class for me was AI4R, and by far the worst – KBAI (please do not waste your time with this class)).

  The good:

  • Professor LaGrow and all teaching staff do their best to encourage the students. You may (and most likely will) feel miserable at times but they don’t want you to. With the number of extra points, reviewer response, etc. anyone who spends the proper time on this course can succeed.
  • The material is quite interesting. Maybe not the very cutting edge but essential.
  • I obtained (or started obtaining) a very useful skill: using AI code generating tools to produce the code that does the desired analysis. I would not be able to write all the code for all the analysis by myself – nor apparently this is needed any longer.

  Not so good:

  • I felt that the amount of material was overwhelming. With its breadth, it was not possible to dive deep enough. Every 3 weeks a super large topic was covered, and in these 3 weeks, one had to (preferably) understand what’s going on, write (or generate) bunch of code, run it on two very different datasets (one of which is huge and super noisy), design and run your experiments that should test a few dozen things and then write an 8-page report.
  • The course puts an emphasis on academic writing. Although undoubtfully an important skill (plus helps to organize the subject knowledge), I believe in this case this emphasis was excessive. I’d much rather prefer to understand the subject matter deeper.
  • There is a lot of debate on the quality of the lectures. Some love this format, some hate it. I would have been OK with it if the lectures were not so long. With the number of things to do for the course, I could not afford to spend several hours a week listening to the lectures. It feels that the same could have been said in a more concise manner.
    As many students complain, the report grading feels random. My grades were 89, 89, 62 (+19 recovered in reviewer response), 101 but the structure of report 3 mimicked that of 1 and 2. The main complaint of Report 3 grader was that there was no a dedicated section with the “numbered” hypotheses (although they were formulated in the Methodology and Data sections of the report). Most likely the need for the separate section was discussed in the OH (that I mostly could not attend) but there was no such requirement in the project description or the FAQs. What I’m trying to say is that I had no feeling at all about what grade I was going to get for the reports (I personally felt that my report 3 was stronger than 1 and 2).

  All in all, I’m not regretting taking this course, just wish it was not taking ALL my time (and more).

  Rating: 4 / 5Difficulty: 5 / 5Workload: 30 hours / week

• 8/lglYFHPFGhVYhCsoaRaw==December 17, 2025spring 2025

  Introduction to Graduate Algorithms

  This class is very exam heavy, so go in knowing exactly what you’re signing up for. Exams are 90% of the grade, while the formatting/logistics/content quizzes are pretty easy and make up the remaining 10%.

  Even if the homeworks are optional, do them. They’re one of the best ways to understand how the exams are structured and what level of precision is expected. Also, attend office hours. They help way more than you’d think.

  If you believe you were graded incorrectly, submit a regrade request. A lot of students don’t do this out of laziness. Just be aware that the entire question gets re-reviewed, so if they find an additional mistake you might lose points too. Still, if you’re confident, it’s worth it.

  You genuinely need to study hard for this class. You can’t rely on Chat GPT or shortcuts to pass cause this course really tests understanding. Proctoring is also very strict, so follow every instruction carefully or you’ll risk unnecessary penalties.

  This class is doable, but it requires serious effort. No slacking. It honestly feels like the ultimate boss of OMSCS.

  For reference, I got 35/60 on the first exam, 49/60 on the second, and 42/60 on the third. If you do poorly on the first exam, don’t get discouraged-it’s very possible to recover if you adjust and study harder.

  One final warning: formatting and wording matter a LOT. The TAs expect answers in a very specific format, and being even slightly unclear can cost you points. One wrong word can mean deductions, so be extremely precise and explicit in your exam responses.

  Overall: tough class, high workload, but passable if you hustle and put in the work.

  -NP

  Rating: 3 / 5Difficulty: 5 / 5Workload: 30 hours / week

• CeYpZ8KCHTq62pxXUF/ZtQ==December 17, 2025summer 2025

  Special Topics: Compilers - Theory and Practice

  ** Took the course in 2025 Fall, not Summer **

  [Background]

  6-th course in program, avg GPA 3.0 before the course. Got C because didn't finish proj 3 and proj 4 (you hardly catch up later phases because projects are continuous).

  [Summary]

  This is a good course, it really teaches you how compiler works with clear objective in syllabus, and a well-phased project (but not well designed for student project, will explain later). Course is well-run, and is harder than average OMSCS courses

  [Video]

  Course videos are well paced. Total length is OK. Videos are quite interesting to watch

  [Assignment]

  4 typed homeworks. Average difficulty, each taking 2-4 hours to complete. Graded very fair and detailed.

  [Exam]

  Only one exam, 35% grade, close book. Felt medium difficulty but did not get good grades, losing points on many details (again, fair and detail graded).

  [Project]

  4 Phase projects that teach you to build a compiler from scratch. I really liked the idea and enjoyed quite some part of it. There is also a well programmed auto grader.

  You need to code at least 2,000 - 4,000 lines for the whole project.

  The reason I don't think the project is a good design is that:

  1. You are almost doing everything from scratch with no guideline, no sample codes, no framework. If you design the framework badly, you will suffer a lot

  2. Each phase builds on the prior phase. If you don't finish the previous block (say you are sick/family/work and miss proj 3), you can't start fresh on the next (proj 4). Even if you have some bug in your prior phase (eg, your parser/semantic didn't handle array well), it will bite you in all subsequent phases.

  I don't personally like the idea of this kind of continuous project. I feel it would be better to provide checkpoints and fresh start for each project.

  [Suggestion to Course]

  1. Consider breaking down exams to 2 or 3, or make it open-notes
  2. Consider making each phase of project more independent. Provide a framework for student to work on. Make it fair for students who did not finish or made mistakes in prior phases.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 13 hours / week

• CeYpZ8KCHTq62pxXUF/ZtQ==December 17, 2025summer 2025

  High-Performance Computer Architecture

  ** Took this course in 2025 Fall, not summer**

  This was an overall OK course, but did not meet my expectation. I also did not do well in this course, got C with 76/100. Due to busy work I did not put enough time in the course. But also harder than the average courses I've taken

  ** Course Content **

  The course covered very wide range of computer architectures, but with less dive deep in the topics, making me have a strong feeling of loss in focus. I also did not see the focus of "high performance" as highlighted in the title.

  In addition, compared with other course, the total course video length is much much loger than other courses I've taken.

  ** Coursework ** 4 projects. Each of them are just running some scripts and fill in experiment results in a word file. You need to modify some codes (100-200 lines each proj) in the processor simulator. Projects are not hard, not time consuming, but less relevant with course content.

  TAs were really good. They do review your answers and codes. Grading on the projects were generous, even if your result is a bit off, you still get points if your code looks reasonable.

  ** Exam ** Two exams, both quite hard in my opinion.

  ** Overall ** It's not an easy course. Also peers who take this course choose a hard track. You really need to spend a lot time and effort to get A/B. There are no curves. You could learn a lot things, but overall I indeed regret a bit taking the course as it's a bit different from what I would expect to learn

  Rating: 3 / 5Difficulty: 4 / 5Workload: 7 hours / week

• B4Rg+t5El3svbUx4Dnp1Nw==December 17, 2025summer 2025

  Game Artificial Intelligence

  Taken Fall 2025, not Summer 2025

  Fun course with really enjoyable assignments! If you took VGD before, you will be familiar with Unity. This class is more scripting in C# compared to that class, and less about playing around with the game objects in the scene (that's already set up for you in the assignments).

  Professor Wilson's lectures are really informative, but he speaks a bit slow and I had to watch the videos at 1.5x speed to get through all of them. There's a lot of videos (some 1-1.5 hours) and it would have been hard to get through otherwise. The textbooks are optional.

  Regarding the assignments, I think I actually spent more time on the earlier ones compared to the ones later in the course. Note that you only get two valid submissions in Gradescope for each assignment. A submission is considered valid if you score at least a 70%. If you have a second valid submission for an assignment, you score is the average of your two attempts.

  Filtering the nodes and edges properly in the grid lattice/graph network for assignments 1-2 took me a bit of time, although a number of helper functions are already provided for you which makes this a lot less hard than it could have been.

  Assignment 3 is implementing A* search from the Millington book which isn't bad, but I recommend making sure your node lists and path recursion are all correct before submitting in Gradescope.

  Assignment 4 walks you through step by step what you have to do, but there is a catch with the expanded geometry that isn't explained. You have to basically add a check to ensure the candidate polygons are not sharing multiple edges at once.

  Assignments 5 and 6 are dodgeball themed. 5 walks you through the steps again, but it takes a fair amount of time to tune to ensure your throws are accurate, especially when going against the Minion. For 6 I wish we actually had to implement the finite state machines ourselves but the starter code is mostly functional. For the dodgeball competition (optional extra credit) you have to tweak it a bit more but for the assignment itself you just have to add a conditional/constructor to ensure the Minion doesn't cross the half court line before throwing in the arena which doesn't take much time at all to implement.

  For assignment 7, you use fuzzy logic to guide a racecar along different racetracks with different properties (one is straight, another is really curvy, another has bends but not as extreme, etc.). This is a HW where you could spend a lot of time just fine tuning things which is a really fun experience. You are graded based on whether you can hit the target speed for the course while not falling off the track more than once and get to decide what enum types to implement (how far to turn the wheel based on the position of the car on the track, and speeding up/slowing down). I did run into a Gradescope issue on this assignment where the car was not falling off as much locally (specifically on the sweepers track) compared to Gradescope but Prof Wilson helped point me towards an implementation with more consistent behavior.

  The final assignment is the only one without coding. You are asked to develop a PCG terrain with three different biomes/features. The main challenge is playing with the rectangle function to separate out the different biomes. As long as none of your features (like the mountains) clip, it's relatively straightforward.

  The quizzes are sneaky difficult, and are a lot more involved compared to VGD. It's less so a regurgitation of the lectures but more practical in nature. They give you a gaming scenario and you have to answer using the concepts from the lecture. Sometimes it involves math, other times it's terminology related. You should do well enough on the assignments that the quiz grades don't matter as much, but it's worth noting that many questions are all-or-nothing. There will be a five point question where you either get full points or 0 based on a correct or incorrect response which can be unforgiving and add difficulty to the course. You can only take the quiz once, so take your time and don't rush.

  Overall, I really recommend this class as an elective. The TAs are really nice and responsive on Ed Discussion if you have any questions. If you do the work, you should get an A.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 12 hours / week

• B4Rg+t5El3svbUx4Dnp1Nw==December 17, 2025summer 2025

  Natural Language Processing

  Taken in Fall 2025, not Summer

  I enjoyed this class a lot, and would definitely recommend it!

  Lectures: Professor Riedl does a great job explaining topics in the NLP space. Of all the classes I've taken in OMSCS, this class has far and away the best lectures. I'm only talking about the first half of the class, because the Meta AI ones that don't come from the professor are not good for the most part (they mostly just read from the slides). The task-oriented dialogue and question-answering modules in particular were brutal. I would replay some of the Meta AI videos several times and still not understand what was being said. Professor Riedl informed us that he plans to record those lectures in the future when he has the time/resources which will make the class that much better.

  Textbook: The class uses "Natural Language Processing" by Jacob Eisenstein (2018). I would guess most people ignore the readings, because they are not really required (like for exams and stuff). I did do the readings, but will admit that certain sections were harder to understand compared to Riedl's lectures. Still, there's some interesting material in the textbook. It goes a bit further on some of the concepts if you're interested in the domain.

  Quizzes: I agree with the post below. They are not too hard. Generally, most weeks there will be a 2-4 question quiz on the module. You have to go through the honorlock system each time which is a bit annoying but not the worst thing in the world. Like in other OMSCS classes, my main tip is for the multiple selection questions do not choose options that you are not relatively confident about because your score for the question gets subtracted for incorrectly chosen options.

  Assignments: The assignments are all in Python notebooks, so you can run it in Jupyter or Colab. You should not have to spend a ton of time to get full points on the majority of the assignments. There are small asks like implementing a neural network via PyTorch's nn.module (the constructor and forward functions) and tuning the model by playing around with the batch and embedding sizes. Due to Gradescope, all cells for each assignment should run within 30 minutes or so. They also provide the test cases so you can compare your output versus what is expected to determine what part of your code is off. I will note for assignment 3B (at least for this semester) you have to implement an attention-based architecture that will only work if you follow the steps outlined in the Attention lecture. It felt rewarding after I figured that out. Occasionally, I ran into issues importing/installing packages but often a restart resolved those problems.

  Final/Mini Project: The final homework (HW5) is a more open-ended mini project. You get three weeks for it, as opposed to two for most of the other assignments. It's a cool concept in that you really get to decide how you want to do the preprocessing (i.e. glove embeddings, tokenization) to improve model accuracy. You also get to implement the training/test loops unlike the previous assignments (I honestly think this should be done for the earlier assignments too, but in those everything is done for you). The recitation video for the assignment posted on Ed Discussion helps point you in the correct direction (they have a video for each assignment), although the pseudocode is not 100% correct and you have to tweak things. It is important to point out that you are not graded on model accuracy, but rather your ability to articulate your decision-making processes and why you implemented your architecture a certain way. My main issue with the final project was that I was running in Colab and kept getting kicked off the GPU towards the end after a couple hours and would not regain access for another day. It is impossible to finish this assignment using CPU alone. Hopefully, the class provides Colab Pro licenses for students at the beginning in the future to avoid issues like this. I ultimately had to switch to Jupyter otherwise I wouldn't have finished. On the plus side, they grade the assignment fairly leniently.

  Exams: The exams are a bit tricky, partly due to the fact that they aren't out of a ton of points. I would say the midterm is more based off the lectures, but the final was a bit different and open-ended. The exams mostly have short answer questions. I think most can be answered in a couple sentences. For the final, we were asked things like how we would develop a dataset for various scenarios, and how we would design the architecture of the actual model. Don't stress too much about the formulas. There will be a question or two on calculating tf-idf, rouge, and bleu scores but as long as it's in your notes you'll be fine. I ended up needing the entire time for both the midterm and final, but I also probably wrote a lot more for each response than what was required.

  Other thoughts: Due to the fact that the final project and final exam had to be manually graded, the TAs unsurprisingly did not have enough time to go through them with a close eye for each of the 750 students taking the class this semester. I would not be surprised if they change things next semester to make things more manageable. Since grades were due shortly after the final exam, students could not request regrades and I'm sure that angered some that were borderline between letter grades.

  Overall: If you get 100s on most of the assignments (not hard to do) and do well on the quizzes, you don't have to stress as much about the exam grades and should get an A. There was not really a curve at the end of the class, but the professor added 0.61% to each student (he added 0.5% to each student's grade, and we were also given half a point back on quiz 4 because honorlock wasn't configured properly at the beginning when the quiz was released). That should be more than enough to get you to an A. Like the student below, I was like a few hundredths of a percent under the 90% before the rounding and finished with an A.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week

• lEeozhOiSvhx9PyqXN1IWQ==December 17, 2025summer 2025

  High-Performance Computer Architecture

  I took a computer architecture course in my undergrad. I did a Copmutering engineering undergrad so we implemented a simplified (aka integer only) version of a risc-v CPU on a FPGA. I didn't learn a lot more in this class. It does teach about a re-order buffer which I didn't learn previously and helped me understand how a lot of modern processors and non-simple embedded processors work so I think the class was still well worth it. It also goes into locking mechanisms for atomic operations, mutexes, etc which I had seen other viewpoints for in AOS, but this had some additional information which was nice.

  But in general if you have done an undergrad course before in general computer architecture this doesn't add much besides re-order buffers.

  Rating: 3 / 5Difficulty: 2 / 5Workload: 8 hours / week

• lEeozhOiSvhx9PyqXN1IWQ==December 17, 2025summer 2025

  Distributed Computing

  FYI this review is actually for Fall 2025, but it won't let me select that as an option.

  To be honest I was a little disappointed in this class. The lectures seemed not as good as other classes I took like AOS, HPC, and HPCA. But I think it does give really good foundational principles for the theory before distributed consensus and the architecture of cloud systems. The projects teach you PAXOS really well, but I feel like they are almost to heavily focused on PAXOS. The last project didn't seem useful for learning. It was essentially redoing parts of the last 2 projects, but was easier this time because you had already done them and knew how to debug them. But it still took like 40 hours.

  There is a curve, but they don't announce it. I had a 85% and got an A. You can easily get a B in this class doing ~10 hours a week. The last 3 projects take a lot of time, but only if you are trying to achieve an A or 100% on them. To get a B on them is relatively easy. For example I had ~82% on the last project within ~15 hours of coding. I spent another ~20 hours debugging (and found and fixed a lot of bugs!), but my grade didn't change. I didn't pass any additional tests I just got closer to passing them.

  The midterm was easy I got a 87% only studying ~5 hours. The final I got a 67% doing about the same.

  I was disappointed in the tests too. I felt like a lot of questions were vague and ambiguous. They don't give any feedback so you don't know what you got wrong or why. They don't really provide any structure to let you know what they think is important in the lectures. For example AOS gives questions to answer for every reading and nearly every other class had quizzes throughout the lecture to make sure you understood core principles. DC just has a lecture and you take a test. No notes, no suggestions of what you should know. It's really odd.

  In general my new job is working on a distrusted system and starting to work for designing towards cloud infrastructure and this class helped with the learning the foundational principles so I'd still recommend it. Maybe a 3.8/5. But I think with some small improvements it could get to like a 4.6/5. I don't think this was a good of a class as AOS or HPC. It's also not as difficult as HPC at all. It really isn't an academically challenging class, the projects just take time.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week

• PoQ3UPp0U2AnwzOB/7IwRw==December 17, 2025summer 2025

  Introduction to Graduate Algorithms

  I ended the course with a B on my first try. The material of this course is fantastic and I think it's a shame that students are discouraged to take this course out of fear for their grades and getting caught in a course retake doom loop. The course has good bones and philosophy but I think it should continue to innovate and incorporate more automated learning because there's only so much manually grading TAs can do for a class of 1000. More automated practice problems with explanations would go far in preparing students for the MCQ section of the exam. Also, releasing some example problem videos for some FRQs during week 1 would help students better understand the material so that they do better on the homeworks and get more valuable feedback. As someone encountering the course for the first time, I often felt I didn't have the scaffolding of knowledge to tackle free-response style questions for a unit until I had seen an example or two solved in office hours which they withheld from us until after 2 weeks into the unit. From a course design standpoint, this seems like an obviously poor decision as most students are floundering around until then and then have to cram in the remaining two weeks of the unit. I am fine falling to a 3.9 from a 4.0 in the program for graduation and have no regrets about taking this course as opposed to the easy SDP course as an Interactive Intelligence specialist who didn't have to take it. Be prepared for extremely hard work and developing an optimal exam strategy if you're going to take this course, however. This course is great technical interview prep and its grading is basically a reflection of the reality of technical interviews. You either gets the problem completely correct when you compete against 1000s of others and move to the next stage or you're discarded. you can hate that - but it's just reality. After this course, you'll eat technical interview questions for breakfast.

  As a baseline, you need to watch all the lectures, take notes, watch all office hours, read all additonal readings provided by the course staff, and review all of your classmates' homework feedback. I loved the textbook and it's an awesome read, but unfortunately, I don't think a lot of its concepts are covered on the exams - so if you're crunched for time don't prioritize it. After that, what really makes the difference is exam taking strategy. This is why students' experiences vary so much from "This course was pretty easy and I worked reasonbly hard, but nothing crazy. What's everyone complaining about?" to "I've taken this course 4 times, work my ass off every time, and don't know what I'm doing wrong, and can't graduate."

  FRQ Section Advice. These are my 3 tips that will help you greatly:

  1.) Getting "Aha" Moments: Acing every FRQ is actually quite simple but I didn't realize the ideal strategy until exam 3 - in which I aced every FRQ. They will say "Do all the problems and you will do well on the exam." That's not the whole truth. There is actually a large group of students who do all the problems and still struggle on the high-stress, high-stakes exams and I think there should be more exposure of the ideal exam-taking strategy to help their scores reflect their knowledge because the optimal strategy can't help you if you don't have the knowledge to begin with. It really sucks when you know the material but can't demonstrate that because you're not understanding the artificial exam environment they've set for you. The exams are a very artificial environment. Every question requires an "aha" moment. If you were approaching these problems in the wild it would be extremely difficult to ace them - however, you're approaching them in the context of this course. I'll explain how you use that to your advantage. From my observation, 6/7 of the FRQ questions were extremely similar to homework problems. All HW, practice, and additional suggested problems are fair game, however. The way to consistently get the "aha" moments necessary is to classify EVERY problem you've ever done for this course by type and list the tricks used for each problem type. You then limit your search of "aha" tricks to these observed finite number of tricks after identifying the problem type on the FRQ section of the exam. There was one exam question which was not directly related to any question we did - it was sort of a hybrid of concepts from 2 problems and was phrased like no other problem. However, approaching that problem with this strategy would work because once you find its problem type and brute force apply the limited number of observed tricks you've used you'll be on your way to the answer.

  2.) Review everyone's homework feedback - not just your own. Now that you're getting the core "aha" moments you need to lose as few points as possible as you get the correct answer. Encourage your classmates to post their homework feedback on Ed Discussion. Review everyone's feedback so that you understand where potential penalties lie so you know how to avoid them. 2-4 points here and there make massive differences overall.

  3.) Never ever brute force a question: Something that is not explained well is that brute forcing a question is graded as if the question is incorrect. I got 4/20 points for answering an free response question with a solution that was correct while others got 8-16 points for answers that were wrong, but more efficient. In this course it is better to be woefully incorrect, but efficient by using the optimal runtime algorithm than it is to be correct but inefficient. I wish they would include a basic rubric alongside each question on the exam to clarify these things when you're having to make these kinds of quick decisions of how best to solve a problem under pressure - but in a way they did because you can learn what the penalties are by reviewing all of your classmates' homework feedback. That was not explained clearly at the time, however.

  In hindsight, if I had known these optimal exam strategies from the get-go, I actually think this course would be an easy A. I would like to see the course expose these strategies because I'm convinced they would rather easily improve student performance and then the course could spend less time with students struggling unnecessarily on the exams and add more content. This course's lectures, teaching staff, and textbook are fantastic. With some innovation - specifically demystifying the optimal exam strategies and adding more auto-graded practice problems for students' practice benefit - I think it could become the best course in the program. I think after implementing the improvements I listed above, it should actually be required in every specialization. The knowledge gained in this course is awesome.

  Bonus tip in hindsight: For Unit 1, if you want additional problems do the Leetcode Dynamic Programming problems and practice writing them in the course format then convert them to code to verify your solutuion.

  Rating: 4 / 5Difficulty: 5 / 5Workload: 30 hours / week

• Cgx+F1RHqMLnuayHYONU7A==December 17, 2025spring 2025

  Introduction to Information Security

  I will preface this by saying that this is my first OMSCS class and I actually took it fall 2025. Given that this is my first class and I have a non-CS background and also do not work in the CS field, I likely had to put more time in than most. I will give the good, the bad, and the ugly of the course for anyone who is new, experienced, or looking to learn.

  The good: The class is definitely a class of very collaborative classmates and TA's who are willing to help when you get stuck. I felt like the TA's in this class were substantially more helpful than any I had in my undergraduate coursework. The assignments were often informative and helpful me in understanding the work. It is also entirely project based, so if you are willing to sit through the problems., you can mostly likely get an A. Also, I found office hours incredibly helpful and the discussion boards were the biggest lifesaver for me. The autograder allowing multiple tries lets you retry submissions and the CTF style of most assignments lets you know you got the right answer before you go onto the next project.

  The bad: The professor for this class is a complete ghost, it's as if he does not exist. I do think this tends to potentially lead to a lack of consistency in some of the assignments and honestly some seemed a lot more valuable than others while others were not very valuable at all. It seems some groups of TA's put in a ton of effort to put together resources to help us and others didn't really seem to care as much. I can explain more in my comments below on each assignment. I think if the professor was active, maybe this could help some of the lacking areas. Also, the lectures are not very helpful in correlating to the assignments but teach general content. They are actually somewhat well written, but can often just be ignored as they don't clearly pertain to the assignments and it's a bunch of videos that are like 30 seconds long.

  The ugly: As I mentioned above, the thing that varied wildly in this class was the quality of assignments. Each assignment has its own TA's who do their own office hours once a week and respond on the discussion boards. Some of the assignments were incredible with super active TA's who were responsive and helped me learn from all my mistakes while other assignments seemed like TA's who just wanted to give a nearly identical assignment to previous semesters and hardly helped me learn anything even a complete beginner.

  Overall, I rated this course as a 3 because for me, it had some very good highs and some somewhat frustrating lows. It might prove useful for newer students or students who want what seems like an easier course to start off that as long as you commit the time, you can get a good grade. I have written some review of the assignments below:

  MITM: Rating: 6/10. I definitely had fun with this one; however, it really did frustrate a lot of students finding the flags in this course. Personally, I think it acquaints you with the Wireshark program, but doesn't teach you any real world uses it might be helpful. Documentation to learn about the protocols provided were not helpful and I Googled everything. TA's and classmates saved my first project here. The KQL stuff was mildly interesting for extra credit, but fairly pointless (Grade: 105/100)

  Machine Learning: Rating: 7.5/10. This is one that statistically people struggled with close to the most and I personally didn't feel that way. The Juypter notebooks provided are helpful an there are tons of resources available in the documentation that I supplemented online to get through this one. Hyperparamter tuning is long and annoying, but everything else was great. I didn't need a lot discussions boards or office hours for this one. (Grade: 100/100)

  Binary Exploitation: Rating: 9/10. Hardest assignment in the course without a question and the statistics given on the GitHub definitely support this (like 40% of students get A's where most projects are in excess of 65-70%). I struggled immensely having no Assembly and taking a vacation during this assignment too didn't help. I learned so much and it was honestly probably my favorite project and the TA's on Ed were incredibly helpful and super knowledgeable. Start early and you'll likely do much better. I didn't learn on office hours for this one. I didn't attempt the extra credit as a I ran out of time (Grade: 100/100)

  API Security: Rating: 5.5/10. Honestly found this one pretty easy after working to find what I was looking for and learned Postman is a pretty cool software. I didn't find the TA's posts super helpful here and had to figure it out myself, but honestly, one of the easier assignments so my comments on the Edboard posts are probably not too relevant. I suppose API's are important, but the specifics here didn't really seem to relevant to cybersecurity (Grade: 100/100)

  Web Security: Rating: 8/10. Postman was another good software here and writing some Python code to interact with API's was fun and I liked the very interactive environment. To me, this seemed like the most obviously relevant project of them all as API's exist everywhere and writing code to interact with them and use them is abundant in real world applications and the XSS and CSRF is some of the most interesting content. It also introduced me to basic Javascript which was cool. I don't recall needing to lean on office hours for this, but the TA's and classmates were very helpful. (Grade: 100/100)

  Log4Shell: Rating: 6/10. This is based on a very important but mostly obsolete exploit. It was a cool project though getting to write some Java code. That said, I don't think it focused much on the Log4J vulnerabilities as much as actually finding a spot that the flag could be exploited through navigating weird filtering parameters that seemed strange. TA's were helpful here and this is the only assignment that even touches Java and gives very few resources to navigate, but it wasn't hard and I never coded Java before. (Grade: 100/100)

  Malware Analysis: Rating: 2/10. Honestly, I thought is project was almost totally pointless and probably the biggest let down of the entire course from my expectations. There was no information given on malware, how to interpret the Joe Sandbox reports which is half the grade, and all the other stuff is a lot of random stuff for running scripts. I found the TA's not helpful here and this assignment really only taught me a few Linux shell commands I had to Google due to lack of content provided by the TA's.(Grade: 89/100)

  Cryptography: Rating: 6.5/10. I found this one both interesting and frustrating in some ways. The stuff on how to crack RSA was pretty interesting, but I found myself mostly Googling ways to crack the encryption rather than actually understanding the math behind what was going on. The office hours were the BEST for this one. It explained a ton about the breakdown of the problem and why it worked how it did. The Vigenere cipher code was also definitely fun to generate as well. I will say though that large numbers in Python can be annoying to work with and will frustrate you at times unless you've worked with it before and you'll have to do your own researching. (Grade: 100/100)

  Database Security: Rating: 7.5/10. I could have realistically only did like half this assignment an still got an A, but I did as much as I could until I got bored and just wanted to give up. I thought the office hours were okay and the TA's were decent enough. The content was definitely good and the feedback on the SQL injections portion were really good and the interference attack definitely made me tinker with data to see what I could pull from it. The office hours was not super helpful, maybe a little bit and I was irritated it was a 2 week assignment and they only had 1 office hours session rather than 2. Student engagement was low because it was the last project and many students hardly had to do anything to get an A. I did appreciate the environment provided, it was actually pretty helpful.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 15 hours / week

• w1SovK8k43rCPMHj+/qlzg==December 16, 2025summer 2025

  Artificial Intelligence

  I took this course in the fall of 2025.

  I got a B, intentionally, I know that the average for this course is ~20 hours per week, but I just didn't have that kind of time. So I decided to lose points when I thought I had learned the material sufficiently.

  Into assignment: 2% this is a coding assignment where you build a stack, I thought that this would be just like a set up a repository assignment so I didn't give myself much time and couldn't finish it. It actually was pretty hard, so I only got 1%

  Plagiarism quiz: 3% free points, this course has super strict plagiarism requirements.

  Assignments: 60% total Only your top 5 are counted for 12% each.

  A1 - Search I got 77% on this, you write search algortihms from scratch, pretty easy to get the first 75% (took a little over a week), but hard to get the last 25% which is the tri-search part which I decided to skip. I spent the most time on this one.

  A2 - Game playing. I spent a week to get 75% on this one, I comfortably did the first 75% in a week then decided to skip the last part again.

  A3 - Bayes Nets, this one was a little tricky, but I got 100 fairly comfortably in a little over a week.

  A4 - decision trees, I got 95, I can't remember much about this one, but it wasn't too hard.

  A5 - skipped

  A6 - HMMS/viterbi, this one was tricky conceptually for me, I spent more time trying to figure out the algorithm than actually coding which is a first for me. Still got 100 in about a week.

  Midterm - open book and you have the whole week, hard though. This took a long time to get 75%, I think the average was around 85%.

  Final - really hard, I only needed 50% to get a B, and ended up with 67% so I'm happy with that. Some info wasn't in the book or lectures.

  Overall I liked the course, but I wish it were better. Most of the projects were on things that I had seen before (search algorithms, HMMs, Decision Trees) so they aren't memorable to me.

  Overall super doable course. I didn't get as much out of it as I did with deep learning, but that's ok.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 10 hours / week

• w1SovK8k43rCPMHj+/qlzg==December 16, 2025summer 2025

  Special Topics: Global Entrepreneurship

  I took this course in the Fall of 2025. I needed an easy course to pair with AI.

  I actually really liked the content and am overall glad that I took the course. As a stem major, I had never taken an entrepreneurship class or even a business class. I though that the information was great, the course really covers why startups fail and how to avoid it by knowing your customers and crafting your product to meet their needs.

  Although I liked the content, it was very limited, as others have pointed out, the entire course is mostly on customer discovery. I would have loved for them to expand to other topics.

  All that said, I have no idea how this course made it into the OMSCS or OMSA coursework, even as an elective. It really didn't have anything to do with computation or analytics. I thought they might have tailored to course to focus on tech startups, but no. It was also incredibly easy. I probably spent an average of 30 minutes per week on this course (I said 1 hour because that was the lowest amount).

  Unfortunately for this course, what you put into something is what you'll get out of it. This course checked a box for me and gave me some good ideas, but that's about it. However, if I could go back, I still would have chosen to take this course. There is only so much time in a day and I needed something that I didn't have to worry about.

  Structure: Your grade is mostly made up of presentations, exams, and quizzes. There is a participation grade as well, but I didn't bother with that and still got an A. The quizzes and exams are easy, you watch a lecture video and then take the quiz. I would watch the videos at 2x speed while taking the quiz/exam because most of the questions were about specifics from the videos.

  The presentations come from the group project, group projects are weird in general, but I got a good group and didn't have to lead the team (unofficially). You mostly do some interviews and then you'll have to do one presentation (if you're in a group) then participate in the final. Pretty easy.

  Rating: 3 / 5Difficulty: 1 / 5Workload: 1 hours / week

• swdenIezNq6PzMls3krsiw==December 16, 2025spring 2025

  Machine Learning for Trading

  I took this as my first course. At the time, I was very critical of it because (like most universities) it expects perfect professionalism and students to go the extra mile, while giving instructors considerable leeway. That’s hard to accept when you’re coming from an even more professional work environment. Examples:

  • Student deadlines are strict; feedback arrives whenever.
  • They suggest refactoring a deprecated predecessor project (Python 2.7, iirc) to help with a current one. Why doesn’t a TA do this once instead of leaving 700 students each semester to do it themselves? Students can’t use AI, instructors can; it would take them only minutes.
  • They ask you to produce output for stock A, but the starter code is pre-filled with stock B. Why?
  • The NumPy / pandas intro is hopelessly outdated. Why can’t someone sit down and make a new video? Sure, it wouldn’t have Udacity-level production quality, but it would massively improve first impressions.
  • I was also very unhappy with the exam. I was well prepared but found myself wanting to argue about the wording of several questions. I got genuinely angry about a couple of them. I’m a terrible exam-taker, but here I felt 100% prepared. Especially since I have a Master’s in Financial Economics and knew the econ/business material inside out. That said, I’ve since completed two more courses (Machine Learning and Reinforcement Learning) and was also reminded of what classes were like at my previous brick-and-mortar schools. All in all, ML4T is clearly above average. The content is very helpful and a solid introduction to ML. I was still answering exam questions in Reinforcement Learning two terms later using knowledge built up in ML4T. My grade was an A. Overall, I’m very happy I took the course.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 20 hours / week

• +nzeCBUmALuR4svysa4e/w==December 16, 2025summer 2025

  Natural Language Processing

  *FALL 2025

  Me: I had an undergrad in CS. I started in Fall '24. This was my 4th semester. I've taken:

  • HCI. High A.
  • KBAI. High A.
  • AIES. High A.
  • RAIT. High B (I was lazy. It's an easier A than KBAI).
  • CN. Low A (I was lazy again).
  • DL. High B (laaazy bones).

  I'm a full-time student. I took this alongside DL.

  NLP grade: A (89.67%).

  It's a good course. You learn alot. The lectures by the professor were great. The Meta lectures were hard to sit through. Some weren't that bad, some were really bad.

  The assignments are informative. They are very easy. It's pretty easy to get 100% on every single assignment and even the mini project. Most of the class did.

  There are quizzes almost weekly for each module. They are easy. Do well on them cuz you don't want them to be the reason you miss out on the A. I missed one and kinda YOLOd others and that almost cost me my A oops.

  There are 2 exams, midterm and final. They each cover the first half and then the second half of the course. I.e. The final isn't comprehensive. The exams are interesting. Study well and you should be fine honestly. They are also open notes. So you can just put any notes you find online in a pdf and upload that and ctrl F throughout the exam. But even though the time given is long, it runs out fast. The notes help, but it's not like a cheat code. Still study. The grading seemed lenient, though that may depend on the grader. But yea, not too hard over all. Averages in the semester were like 80% or higher for both.

  TL;DR: Assignments are easy. Quizzes are easy and almost weekly, don't skip them and do well. Exams are kinda easy too, just study the material well and have good notes.

  Courses I've taken in order of difficulty: AIES (1/5) < CN (1.5/5) < HCI (2/5) < RAIT (2.5/5) < KBAI (3/5), NLP (3/5) < DL (3.5/5).

  Rating: 4 / 5Difficulty: 3 / 5Workload: 8 hours / week

• +nzeCBUmALuR4svysa4e/w==December 16, 2025summer 2025

  Deep Learning

  Oops, my review below was meant for Fall 2025 btw.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 12 hours / week

• +nzeCBUmALuR4svysa4e/w==December 16, 2025summer 2025

  Deep Learning

  Me: I had an undergrad in CS. I started in Fall '24. This was my 4th semester. I've taken:

  • HCI. High A.
  • KBAI. High A.
  • AIES. High A.
  • RAIT. High B (I was lazy. It's an easier A than KBAI).
  • CN. Low A (I was lazy again).
  • NLP. Low A (laaazy bones).

  I'm a full-time student. I took this alongside NLP.

  DL grade: B (86%).

  It's a good course. You learn alot. The lectures can be a bit convoluted. Sometimes things aren't explained well or clearly enough, so supplementary videos may be needed.

  The assignments are informative. Not too difficult, but running models for the last assignment took time. But over all, the assignments are fine. Start early and you'll be good. There's a report for each assignment, so don't mess that up. For A4, I messed up the report alot (cuz I was lazy and started late but also the guidelines weren't too clear) and that cost me my A oops. But yea, start early and getting 92+ on each assignment should be very doable.

  Quizzes are the most annoying part. Some people did well, but I'd procrastinate and leave everything for the last day so didn't do that well. But even those who studied struggled, so I think the quizzes are just hard. They're worth almost 20% though which is annoying. I'd recommend just going over the lectures a few times, asking chatgpt to clarify things if needed when studying. Then just hope for the best and take it. Many people studied for waay too long without significant gains in performance. They get harder as time goes on so try to do well on the early ones. Pay attention to the focus topics post on Ed that comes a week before the due date.

  There's a group project. For me it went really well, we got 100. So just get a competent team and you'll be good. The project was pretty easy. Assuming everyone puts the work in.

  If you do well on assignments and the project and do decent on quizzes, an A should be pretty easy ngl. Just try hard enough.

  No exams.

  TL;DR: Assignments and reports can be time consuming but not too hard. Not hard to do really well on them. Quizzes are hard, but do well on assignments and the project and you'll be good. An A is definitely veeeery achievable.

  Courses I've taken in order of difficulty: AIES (1/5) < CN (1.5/5) < HCI (2/5) < RAIT (2.5/5) < KBAI (3/5), NLP (3/5) < DL (3.5/5).

  Rating: 4 / 5Difficulty: 4 / 5Workload: 12 hours / week

• VgBZ5q2cK2PdyQCErWPMmA==December 16, 2025summer 2025

  Machine Learning

  I took this course in Fall2025 for the third time (got A) and as the last one for my ML track (yes i lost interest in prev versions and withdraw immediately for very obvious reasons) and i agree it's been improved a lot. Now contents are more up-to-date (with some new videos and practical homeworks!), useful and also with enough depth (thanks to reports, though tough, they are really worthy of time). It's really nice to have centralized threads inside Ed and also there're nice people summarizing OH (which could be done by TAs using LLM nowadays to save time for everyone.) Yes the scaled up course is still alive and evolving!

  I hope there're more up to date (optional) videos on practical aspects of the whole ML pipeline and also an industry talk on modern infrastructures, monitoring, and risk management, as it's becoming even more frequent and critical nowadays as everyone touches ML from various aspects. i wish the homework instructions could be even more concise, elegant (modularized, not of spaghetti structure so we dnt GOTO previous papges to callback), and with good academic sense (to write a report of 8 pages i hope instructions/OHs all together are clean/neat and LESS than 8 pages). If you can save 5 hr for one student, then it's 5000 for 1000 students in one year, which is highly impressive. Quizs are necessary and well designed, i can't complain. Just the scientific calculator makes me a bit awkward cuz i really dnt have them nowadays...

  For incoming students, you should be aware that ML is wider than ANN and deeper than DL (actually Stanford CSMS could skip ML 229 and go for DL 230 directly). So people attending this course should expect a harder mode compared to DL/RL. HWs are necessary as a CS course, and this course is more of experiment report/conf paper writing style, which is a very good training for both research work and phd application (if interested and willing to take the challenge.)

  Rating: 4 / 5Difficulty: 5 / 5Workload: 20 hours / week

• j+rann0g9HeMxS4wpX+LXw==December 16, 2025fall 2024

  Data and Visual Analytics

  I was completely swamped with my job when I took this course and I therefore didn't put much effort into it at all, and I did not do well in it because I refused to cheat to complete the assignments. That being said, it would have been really easy to cheat and complete the assignments correctly to get an easy A. Beyond the grade, though, the course is beyond awful, and the funny thing is that everyone knows it but nothing has been done about it. There's nothing much I can add beyond what people have already written in this forum and what your classmates in your group are probably saying about it. This course needs a revamp so badly that it honestly should be the first revision made to the OMSA curriculum once the powers that be finally get to it

  Rating: 1 / 5Difficulty: 3 / 5Workload: 5 hours / week

• j+rann0g9HeMxS4wpX+LXw==December 16, 2025summer 2025

  Data Analytics in Business

  Dr. Xu's lectures are pretty much gold. They're structured really well with clear planning, he has amazing delivery, and he explains the concepts really well. I would say that these lectures are clearly better than Goldsman's and Sokol's, which are generally considered the gold standard in OMSA. He also does give clear direction for several avenues in these lectures as well for the ambitious student to further their learning beyond the required HW. However, I just think that the course is overall a bit too easy. I think the course would be much better if it were just a bit more difficult, e.g. if the assignments weren't so similar to his coding tutorials, if there were even some quizzes, etc. I think I would have gotten a lot more out of this class if some of the graded work required me to struggle a bit more with the concepts

  Rating: 4 / 5Difficulty: 1 / 5Workload: 4 hours / week

• d27Y/joY1HRdqNilhWvq1w==December 15, 2025summer 2025

  Introduction to Graduate Algorithms

  Ok, I'll show results first:

  Final grade: A Exam 1: 36/60 (C) Exam 2: 58/60 (A) Exam 3: 58/60 (A)

  Logistics quizzes: 100% Format quizzes: 100% Content Quizzes: 80% Homeworks: 40%

  Review: This class was difficult because I at first didn't know how best to approach it. As this was my last class, I wanted to coast into the sunset. I kind of assumed that if i gave a low-A effort i would end up with a low A. That is not the case. A low-A effort gives you a C or D and you will repeat this class. In order to succeed you must master the material and that means you must absolutely give it 100%. This class is not for the faint of heart, and it is difficult if you have a full-time job and other commitments/ a family.

  Overall i truly enjoyed the material in this class, and because it was so harshly graded, it forced me to fully understand it to make it through.

  Tips:

  1. The exams are like the homework. They are not easier than the homework - do not fall into the trap of thinking exams are easier than the homework. So if you do not know exactly how to do the homework you were given, as well as the proper format of the answer, you will fail. You MUST be able to do all assigned problems from the text, as well as the homework problems, with your eyes closed. I don't care how many times you have to manually solve and write out the answer, but do it until you get it.
  2. Use flashcards. I downloaded Anki (and paid for the iPhone app) after my disastrous first exam. i was super upset with my grade and thought it was harshly graded, which it was. but while it was harsh, it was fair. Create a flashcard for every concept and algorithm you will use, and study them almost every day. reinforce the material until you can write out all algorithms, their runtimes, the rubric for answering a question, etc. by memory.
  3. go to all office hours. This is a huge time commitment, since Joves's office hours are like 5 hours long. They are incredibly helpful (thank you Joves, you truly go way above and beyond)
  4. Before an exam, know in your heart that you have internalized and memorized the questions you have been assigned (book + homework), and that you know from memory all the concepts joves mentioned in his office hours
  5. Do the homework. you will likely fail each and every one of them. They do this so that you learn, don't take it personally
  6. Embrace the suck and keep going. This class is stressful, so don't beat yourself up.

  Things that could be improved about the class:

  1. The lecture videos don't begin to prepare you for the exam. Dr. Vigoda's lectures are amazing, but they're not enough. That means Dr. Brito and the TAs have to do a bunch of extra lifting, which they do.
  2. Joves office hours are amazing, but it's crazy to think what would happen to this course without him. This is a huge risk.
  3. The coding homework has hidden gradescope tests. it would be cool if you could see them beforehand. i want to learn the material and how to best implement an algorithm, not spend hours and hours of time building unit tests for edge cases, especially when this course already takes so many hours.

  Bottom line: -The material in this course is fantastic -Dr. Brito is great, and I really appreciated his weekly office hours. Thank you, Dr. Brito. -Joves's office hours go way above and beyond. Thank you Joves. -All TAs in general were amazing. They were all over Ed, helping students the entire semester. Thank you TA team. -This is a brutal class, especially if you don't have a ton of time and if you haven't taken an algorithms course before. Don't think you can cruise your way through this class.

  LASTLY -You can recover from a bad exam. I got a C in my first exam, and ended up with an A overall, so I'm proof of this. Don't think that you're dumb and lose faith in yourself. Brush yourself off and keep going. Don't drop for any reason, because you will have to retake it anyway - if you drop you will lose out on material post the drop date.

  Good luck...

  and with that, I'm out! 5 years of OMSCS - what a ride.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 25 hours / week

• cH7US12FEoevhaxrYr7j+w==December 15, 2025summer 2025

  Advanced Operating Systems

  Fall 2025 - Good course overall, Professor Kishore has a lot of passion for the topics which I think rub off on students. There is a weekly zoom call which students can join and take part in polls for extra credit.

  I also had good experiences with the TAs.

  I enjoyed the projects the most, the second one required writing a paper which took the most time. The 1st and 4th took around the same amount of time. While the 3rd was the shortest.

  1st and 4th project - about 40 hours. 3rd project - about 25 hours 2nd project - about 60 hours, mostly due to me wanting to write a very good paper, which might not have been necessary

  I think the lecture content is a bit too long. But as other students said some time can be saved by not reading the papers. 80% of the questions are released ahead of time and usually. If a question doesn't get covered in the lecture it will usually be found either in a paper quickly using CTRL+F, or in a hangout session which was previously recorded.

  Even with 80% questions released there is only 3 days to answer these questions. So some studying is still required to have a general idea of where to look for an answer. There is collaboration between students for the test answers, usually if a question has a big consensus it's probably the correct answer. For other questions with a variety of answers trust your own answer you found from the material.

  Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week

• OZlrpCV446ardu50Pw02Tg==December 15, 2025summer 2025

  Graduate Introduction to Operating Systems

  [Disclaimer] I took it in Fall 2025, but the option is not available when adding review.

  Grade breakdown: 94%: Midterm: 85%, Final: 87%, Projects: 102%, participation: 100%

  Time breakdown (+/- 10%): Project 1: 60h, Project 3: 40h, Project 4: 40h, Lectures: 30h, Reading papers: 30h, Midterm preparation: 10h, Final preparation: 25h

  Hours spent/week: 12-18h

  Software engineering (3YoE) with python and C# experience . Non-CS/Non-STEM background First OMSCS class

  My journey and recommendation:

  Before the course starts I spent about one month before the term working through Beej’s Guide to C Programming and Chapters 1–6 of Beej’s Guide to Network Programming. I aimed to gain a basic familiarity with the material, and it helped tremendously in reducing stress once the projects began.

  Projects: I generally follow a three-stage workflow: reading instructions, drafting the design, and coding.

  1. Reading instructions:
I read the project instructions and the provided codebase twice over the course of a few days, highlighting and summarizing key requirements and constraints.
  2. Drafting the design:
I then spend another 3–5 hours with pen and paper sketching my design—drawing, erasing, and redrawing as needed. At this stage, I focus on identifying the necessary components, defining the inputs and outputs of each component, and checking whether all components can be connected logically. I intentionally leave implementation details blank.
  3. Coding:
Finally, I implement the design from the previous stage. Inevitably, the initial design may miss some requirements, and I may need to add, remove, or modify components. However, having a design in place helps keep me focused and moving in the right direction.

  That said, it’s also perfectly valid to code first and let the design emerge later —each project is different, and you should choose the approach that works best for you.

  Writing project reports You are required to write three project reports, each worth 1.5% of your total grade, with a maximum length of 12 pages. I highly recommend using flowcharts to illustrate your design and thought process. Additionally, the instructions note that the strongest reports are concise and clearly explain the author’s reasoning. I can confirm this from experience—my reports were ~6 pages but I clearly explained why I chose one design over another (time constraints count).

  Writing project reports (continued) Sometimes, it is more strategic to deprioritize certain report components in favor of exam preparation (each exam is worth 25%). For example, I chose to skip the flowchart in the last report (worth ~0.3% of my overall grade) and instead used that time to review course material for the final exam.

  Exam preparation Complete the practice exam and carefully review any course topics you feel unsure about. Many topics from the practice exam appeared on the actual exams. Since the exams are closed-book, it is more effective to understand how to derive formulas rather than relying on memorization.

  Mental Health I tried to cap my weekly study hours to 15 hours. It helped me stay focused and prioritize materials that had the most impact on my grade. I also intentionally spent about 5-10 hours a week on my hobbies to maintain positivity and avoid burnout. I used Flora to track time spent (non-affiliated); any other time tracking app works too — I just love free apps :)

  Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week

• gfiGuaL+UAyy5nXKbM5cOw==December 15, 2025spring 2025

  Deep Learning

  *FALL 2025

  The past 4 months of this course have been super engaging and intense, but i have loved every single second of it. All of the readings including the Deep Learning books and the papers in all assignments were fantastic! I haven't learnt more in a course ever before (even outside OMSCS!). Got to learn a great deal from the group project too!

  I have only been through 4 OMSCS courses yet, this has been the best of all so far!

  Rating: 5 / 5Difficulty: 5 / 5Workload: 30 hours / week

• +1a6SpmnnTBrF9Suw4cPsQ==December 15, 2025summer 2025

  Graduate Introduction to Operating Systems

  [DISCLAIMER] Review was for Fall 2025, but semester is still not present.

  I tracked the whole time that I spent on this course. It should give a more accurate perspective to upcoming students that may want to enroll into GIOS about the time expectancy. The total that I spent on this class, between the projects, tests, lectures and reading posts/chatting on slack (I didn’t always track this latter one) was… dumbrolls... 210 hours. This was for Fall 2025. Given that the class ran for about 16 weeks, the average is ~13 hours per week.

  This number could vary depending on your background and previous experience. If you’ve worked with C/C++ and/or gRPC before, some of the projects could take less than they did for me. On the contrary, if you aren’t that experienced with some of these technologies or come from a non-tech background, the time expectancy might increase.

  As for myself, I listened to other folks' advice and read a book about C (BeeJ’s Guide to C programming) before enrolling. Though I ended up also reading half of the BeeJ’s Guide to Network programming during the course (trust me, you will need this one). My recommendation is the same as others have stated before, learn C prior to taking this class if possible. Though it’s still doable learning on-the-fly, it could certainly be a more frustrating experience. As said, BeeJ's Guide to C programming is an excellent resource. You don’t need to read the whole thing, only the basic stuff. Then you can jump to the Network programming guide. Before or during this class.

  As per the content itself, I can’t say anything bad about it. I’ve learned a lot of stuff, both practical and theoretical. I will list some of the practical ones I consider the most relevant for my career: I learned C, honed my abilities in C++, learned and applied gRPC concepts, learned to think around and use threads properly, learned distinct IPC mechanisms and know what happens under the hood now when my programs run.

  Ended up getting an A in the class with a final 91 score. If you do everything well, you shouldn't have much problem with getting 100% 's across all 3 Projects. But please, start the projects as early as possible. I learned this the hard way with the 1st one. As per the midterm and final, study well for these exams, as they are about 25% of your score each. If you get above 85 (final class score), if I’m correct, you are guaranteed an A. There’s a curve that might help you bump up your grade letter in case you get short though. This semester the minimum for an A was 82 for example (the curve changes from semester to semester, depending on the average across all students).

  Overall, excellent class and excellent material. I loved spending time chatting with random folks in slack, getting frustrated over gradescope complaints and sharing those frustrations along with my classmates. This is one of the best classes in the OMSCS program. If you’re going for the Computing Systems specialization, highly consider taking it.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 13 hours / week

• R9YJJzBXzFTCeDom1vLnDg==December 15, 2025fall 2024

  Software Development Process

  My experience was generally consistent with past reviews. Worthwhile if you have no/limited agile development experience, like coding in Java, and want an easy class to pair with another. I actually think the course was well organized, the lectures and assignments were reasonably well designed, though seriously outdated (e.g. group project is an android app coded in XML and Java).

  The group project itself was a mixed bag. The instructors did a reasonable job pairing people with complimentary skills in my group but regardless I just think groups are hard in an online class. The project itself was a missed opportunity to create something that someone would actually want as a portfolio project.

  Another gripe I have is the strict weekly release and submission schedule making it hard to organize the class around other commitments and weekend plans.

  Rating: 3 / 5Difficulty: 3 / 5Workload: 12 hours / week

• X+l5z5kVSTD1N/aah19JWA==December 14, 2025summer 2025

  Machine Learning

  I took this class in fall 2025. My background is in CS but I've never really done ML at all before this.

  Thoughts: I enjoyed this class. I feel pretty prepared to speak confidently at ML because it. I think people may feel a little frustrated at the "self learning" and open-ended project but thats what made it truly feel like a graduate class. Also, the TAs and office hours are a great resource.

  The lecture format is a little hard to follow along with, I suggest getting the topics from there and then reading the book with those topics in mind.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week

• xJQ8fGaLma44cl2jyMwKfA==December 14, 2025summer 2025

  Graduate Introduction to Operating Systems

  Non STEM Undergrad + CS Minor. First class in the program (Taking In FALL, not Summer, option for Fall is not released yet). Ended with a high A without too much trouble. There is not a lot of "small assignments" scattered throughout but more 3 major projects and 2 exams to watch out for. If you get them done quick you should have no trouble in the course. Study (and participate in Slack) and you will be fine. The lectures are wonderful and the projects are.. okay. You WILL learn a lot and if you are not a CS undergrad I highly recommend you take this class. If you are.. well, this will mostly just fill some gaps (I took Systems courses in Undergrad).

  Rating: 4 / 5Difficulty: 3 / 5Workload: 11 hours / week

• 9yVqyEJRGfFtlYvEuNEz6Q==December 14, 2025summer 2025

  Graduate Introduction to Operating Systems

  [DISCLAIMER] This review was for fall 2025, but semester is still not present. A great experience, I learned a great deal. Math undergrad. If you ever do something remotely related to backend this course will be great help. You will learn how nodejs works and many server architectures, like ecent driver, multi process, multi threaded.

  I studied/worked a great deal for this course. Ended up with 95%.

  P1: Really long, but doable. Be good at c and try to complete something related to the first chapters of beej’s guide. ME: Not hard, but really take your time to learn the concepts well, do not try to memorize just for the sake of it. P2: A bit harder, this actually made me think and design a solution from scratch, but totally worth it. Not very time consuming. P3: The hardest project, really take your time to design and test well your solution. It’s doable 100% though. FE: It convers a lot of material. Some papers are really hard to read, others are really to read. The main difficulty was to remember all the contents.

  My recommendation: Do this course and learn every lecture of it, it will teach you how computers work in a very abstracted and comprihensible way. I dedicated a great deal to it but if you put the time, the results will come, have faith.

  Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week

• aaDyK4hzd2TIbtGAc9NAWw==December 14, 2025spring 2025

  High-Performance Computer Architecture

  Personally, I loved this class. It filled a lot of gaps in my hardware knowledge. Although the main focus is CPU architecture, I learnt a lot of practical concepts I use day to to day in my current job: pipelining, instruction scheduling, branch prediction, predication, compiler ILP, virtual to physical address translation, TLBs, how do caches work in multi-core setting, and finally synchronization in multi-core. Anybody who is serious about systems or HPC should take this class!

  Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week

• +iv6S115mdUf4/Of+/+4Sw==December 14, 2025summer 2025

  Machine Learning

  This review is for Fall 2025

  This class was extremely frustrating to me. Unless you've already had an introductory machine learning course before this introductory machine learning course, you're going to have a bad time. They say in the syllabus that you need to be proficient in python, but you need to be proficient in python and two specific libraries. You need to have used Scikit-Learn and PyTorch at a minimum. The other libraries you'll use aren't difficult to learn how to use for your reports, but if you are completely unfamiliar with those 2 libraries, then you're going to put in more work than what I've listed on this review. The reports are fine. They are a time sink to complete well, but they are not the main problem. If you come into this class with minimal knowledge on how to operate the python libraries, you will have to learn the material, learn how to write it up with the library that you will have to learn, and then write up the results in LaTex. The reports don't always cover the lecture content so you'll spend the majority of your time learning about the things to write about in your report followed by quizzes over all of the video lectures and readings. The reports breadth becomes anti-pedagogical.

  The TAs and professor are great, passionate, and a treasure trove of knowledge, but after the first report is due, it felt like their time was consumed by grading the reports. Most of my questions went unanswered or answered by other students. Before this stage, I felt like my questions were met with more factual responses. For example, I asked about how a specific cross validation method worked and to see a mini example of it, but I received a "yes this cross validation technique is used for this algorithm" answer. This semester they added the reviewer response so you could edit your report, but I didn't have time to correct the reports due to needing to work on the next report and learn the next material.

  In general, the material is great. You will learn a lot. The reports are a fine pedagogical tool, but due to their breadth they are anti-pedagogical to some concepts in the course. You will spend most of your time writing code, waiting for results of your code, writing what you can of your report while you wait, and write about the results of your code. You will rinse and repeat this cycle for many algorithms. My overall recommendation would be to watch the material for the public version unless you are aiming for this specialization for your degree. If you do end up taking this course, you will probably want to watch the public versions first anyways. The class is doable, and you can succeed. My experience boiled down to two factors: the amount of time I had to dedicate to the class and how much frustration I was willing to tolerate writing reports.

  Rating: 1 / 5Difficulty: 4 / 5Workload: 20 hours / week

• iUWbT8XDGbkLRhs16wUEhg==December 12, 2025summer 2025

  Educational Technology: Conceptual Foundations

  I went into this course with high hopes. I did actually learn a lot and enjoyed my time. However, the course is set up in an extremely poor manner. No clear rubrics for assignments, many students got no feedback from their mentors, and while the assignments at the beginning provide a learning experience, they also contain quite a bit of busy work. Unfortunately, the grading effort I experienced from my mentor was very disappointing and inconsistent. I had a death in the family and didn't finish one of the first assignments (my problem), and my grade wasn't great. However, my mentor gave me the same grade for the same type of assignment that I put 2x the effort into. I resubmitted the first of those assignments and received an A, by adding barely anything and still leaving parts incomplete. The grading of this course is a joke. The course is big on incorporating peer reviews, which I found to be equally poor when it came to getting feedback. The peer reviews often focused on criteria that was made up by the peer reviewer. The projects are created by the students and approved by their mentors. I got a peer review saying that the testing for learning outcomes in my project was not sufficient. I did the development track, not research, no experiment created. Such testing was not part of my proposed project or one of my goals for the course. This course could be amazing with an overhaul.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 18 hours / week

• o6S8rstKct8Vt6oBmGAUKA==December 11, 2025spring 2025

  Introduction to Graduate Algorithms

  Not a difficult class conceptually, but made artificially difficult due to needless bureaucracy, inconsistent grading, poor communication, and punitive grading. You better say exactly what the rubric is looking for, and there's no way to know ahead of time what that is. TAs and staff should take more accountability for the amount of people complaining. I say all of this as someone who is familiar with the material already.

  Rating: 1 / 5Difficulty: 1 / 5Workload: 10 hours / week

• NIGJZsk80tUgZnG5pO8bIQ==December 10, 2025summer 2025

  Human-Computer Interaction

  Graded on participation, quizzes, tests, homework assessments (long writing pieces), individual projects (25 pages), team project (40 pages)

  Rating: 5 / 5Difficulty: 5 / 5Workload: 5 hours / week

• nDdwarmsB6c18RBejfPSgw==December 10, 2025summer 2025

  Machine Learning

  This review is for Fall 2025, which is not available yet.

  My background: Dabbled in undergraduate data science projects without much theoretical grounding. Bachelor's in liberal arts (majored in computer science).

  The good: The spread of the content was useful to touch upon almost all areas of traditional ML: supervised, unsupervised, optimisation and reinforcement learning. However (perhaps because of my lack of background), I felt that cramming optimisation and reinforcement learning along with all the report writing and code needed to set up the experiments meant that I ended up with an only superficial understanding of the content.

  The structure of the assignments was also useful to learn how to set up ML experiments, and deal with the messiness of dealing with real life datasets. Definitely learnt the importance of code abstraction and writing my own custom wrappers by the end of the course as there are many re-runs of the algorithms needed to experiment and get the correct plots.

  The reading quizzes + final exam forced me to actually sit down and understand the concepts covered, albeit from an ML intuition point of view rather than anything mathematically rigorous.

  The not so great: The lectures were great (I appreciated the banter and conversational style of lecture that really tried to build conceptual intuition rather than just going through equations), but unfortunately too verbose especially when there are only 3 weeks between assignments (which includes completing one reading quiz per module, watching all the lectures, coding the experiments, and writing the report). Resorted to ChatGPT to explain and understand the concepts.

  While I appreciated the approach to the assignments (see above), I felt that the assignments had too many requirements which sometimes led to assignments being checkbox ticking exercises. For instance, for the SL, UL and OL assignments, we had to run the same analysis twice on 2 different datasets, one being much larger than the other. While perhaps the purpose of this was to teach us how to do things in a scalable manner, it was probably unnecessary in a course which is also trying to teach more fundamental intuition of understanding the algorithms. Perhaps AI would be a more suitable course since it's focused on the implementation of the algorithms.

  Note: The course seemed to me to be front-loaded - the SL and OL assignments had the most requirements while UL and RL was more open-ended, while the final was MCQ only with no numerical calculations required.

  Rating: 3 / 5Difficulty: 4 / 5Workload: 25 hours / week

• FP5L6mWaUT0wvGT1G1f+xg==December 10, 2025summer 2025

  Deterministic Optimization

  This is for Fall 2025.

  It's a great course. The lecture is well organized and teaches many interesting techniques and algorithms for solving various types of optimization problems. The weekly homework and two exams are well designed. Some of the exam questions felt a bit hard but overall closely followed the style of the practice tests. As long as you do homework and review the lecture, you'll do well. I agree with other reviewers that this class provides a good theoretical foundation for other harder ML classes like CDA and DL.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 8 hours / week

• LRpToCcdlss3cD/h405CIQ==December 10, 2025summer 2025

  Statistical Modeling and Regression Analysis

  I was worried about scary reviews but they reorganized this class in 2025, and things improved a lot by the time I took it. Overall it was a nice chill class with rewarding materials. I liked how homework had lots of hands-on coding exercise. It helped me solidify my understanding of the concepts. Also I appreciated how they allowed a choice between Python and R. I went with Python, and it was generally smooth. Overall really positive and productive learning experience.

  Rating: 5 / 5Difficulty: 2 / 5Workload: 7 hours / week

• JIVFYMl9bTl8gFjMBTZk8w==December 9, 2025spring 2025

  Advanced Operating Systems

  (Fall 2025) Solid class. The first portion felt like a review of GIOS/HPCA, and the rest of the course was an overview of OS techniques/innovations presented through academic papers. It improved my system design ability, especially for distributed systems.

  The lectures were easy to follow and engaging. Kishore is a great professor. If you have the time to go to the hangouts with him, I found it useful to reinforce lecture topics (plus extra credit points).

  The projects were good. My ranking of difficulty (hardest to easiest) is P4 > P1 >> P2 > P3. P4 and P1 took a significant chunk of time while P2 and P3 could be done in a couple days. P1 you build a CPU scheduler/memory manager using libvirt, P2 you implement some barrier algorithms and write a report on their performance, P3 is a introduction/review of asyncronous gRPC, and P4 you implement MapReduce using gRPC. There is an option to work with a partner. I did not and don't think a partner is necessary. The projects are long (esp. P1/P4), but IMO not so long as to require a partner. Doing them solo also helped my learning/programming ability. Getting help on the projects is done through Ed, and it was mostly students helping other students, not TAs helping students like GIOS.

  I did not like the exam format. 80% of the questions are released ahead of time on Friday night and the exam is due Monday at midnight. Students can collaborate on the answers to these 80% of released questions. I felt like the exam format encouraged memorizing answers to the 80% of released questions and crossing your fingers for the other 20%. They are also all open response questions so you can't BS an answer you don't know. The exams get easier as the semester continues.

  I think the difficulty of this course is overstated. There are really only 4 papers required for the projects/assignments (2 for P2 and P4, and 2 for the paper summaries). There are 90+ papers - reading them all is not a good use of time when the lectures/hangouts review them enough. I was able to pair this course with CN while working full time.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week

• Zipr8wSl9cgM/YJwGrdmYg==December 9, 2025spring 2025

  Introduction to Health Informatics

  Fall 2025.

  I found the content to be pretty interesting, and I enjoyed touching some tech that I've never used at work. Assignments are released early after the first week, which is nice if you need some more flexibility to work ahead. Time commitment is mostly low--around 5-7 hours per week until you start working on the group project, at which point it can either increase or decrease depending on your group & project.

  Grade is made up of labs, quizzes, and the final group project. Of those three, the quizzes and labs are mostly gimme's. The quizzes are open-book, and the actual coding in the labs is incredibly trivial. Lab set-up can definitely be annoying, but to be honest, ChatGPT now makes troubleshooting the problems so easy, I don't really consider them to be real obstacles anymore.

  The group project is a typical group project with vague specifications; I recommend choosing a topic that solves a problem to make framing your project easier. Find a decent team early on with a spread of experience in backend, front-end, and DevOps to help with deployment. As always, be very clear about Git workflow rules if you don't want to be stuck on Git clean-up duty later on.

  Overall, class is not as bad as all the bellyaching might have you believe. I learned something new without having to lose too much sleep!

  Rating: 4 / 5Difficulty: 1 / 5Workload: 10 hours / week

• rpAHA6YUtihUHkejGN+aZg==December 9, 2025spring 2025

  Artificial Intelligence Techniques for Robotics

  Took this course in Fall 2025. Overall this is the best course so far I've taken at Gatech (I've completed 5 courses). I end up with an A, not even trying too hard cuz for both midterm and final term I only use one chance of two since I was too busy at work. I would say this course is Organized, Supportive, Rich in Content and having Manageable Workload.

  My background: No previous CS background, only took a few fundamentals programming class, SW development class and ML class during my master degree. Which means I don't have systematical learning experience as well as strong skills in math. But yes, I'm quite familiar with python, have basic knowledge of matrix operation, limited knowledge of probability. Those things are enough to support me to understand the lecture and keep learning during the semester.

  Class Content: Well structured. I won't say it is very updated to the trend but they are very useful and the lesson is well polished. You will learn basic algorithm of robotic (self-driving) which is widely applied to different areas. I took some note during the lecture and by reviewing the note, it's easy to grab the main knowledge quickly and apply them in the hw. Personally, I think it is an interesting class, neither too hard nor too easy.

  Pset and Project: Small Pset is pretty easy and will only take 1-2 hour as long as you watched all the course video. And there are videos going through the problem set which you are allowed to reference in your code. They are also very helpful as most of the content will be related to the project and exams. Project will take relatively longer time but they also allowed you longer time on them. Most of the project took me around 10 hours, some of them are shorter. But not all 10 hours are coding, you may spend time watch the office hour recording where the TA will walk through the long project pdf and give you a few useful hints. After you take time fully digest what is the problem about, the coding time could be really short. And then, another time-consuming thing will be tuning the hyper-parameter. This could different you from get 85/100 or 100/100. If you are lucky, or you chose not having those 15points, you may use way less than 10 hours in each project (maybe only 6 hours). However the final 1-2 points sometimes really took time. It depends on your decision.

  Exams: Instructor will give you some helpful tips on review. I would say as long as you watch the lesson video, did the project on your own, went through the knowledge mentioned by the instructor, it should be easy to get a 80/100 in both exam. Nothing far beyond the course and project. Both exams have 2 chances, I only took once as I was busy but still the grade is quite enough for me to get an A.

  Instructor & TA: Very supportive. Most of the time I won't post anything on edX. But in this course, I've taken the discussion on edx as a main learning and Q&A resource as the stuff team will carefully answer any question especially regards project. Their response speed is also very quick. And most of the questions I have during the project, I can find the answer in the discussion before I even asked.

  Overall, really recommend this class.

  Rating: 5 / 5Difficulty: 3 / 5Workload: 10 hours / week

• u+tE5isQhCiKw8VwZIuu7w==December 8, 2025summer 2025

  Artificial Intelligence Techniques for Robotics

  I took this course Fall 2025. I got an A (90~%). BG: BS in CS. 2 years SWE experience. This was my first class in the program. The lectures were okay. The instructor does NOT go into much detail. He tells you what needs to be done and lets you implement it in python, but there isn't any in depth theory or the algorithms which is a bit disappointing. HW: You're given the answers and can use them as is, which helps a lot. Projects: I found the projects to be the most challenging part. They were very much doable tho. Ed discussions and code reviews (I had 2) helped more than anything for projects, but make sure you read them frequently!! Even though some of the projects were hard, I enjoyed them, especially the particle filter one. Another thing, you can re-submit as many times as you'd like on Grade Scope. Exams: Pretty straightforward. Study the basic theory behind the concepts and you're good. There's no coding. Is it good as your first course? Yes, but you need to know python. Is it easy? Not entirely, some parts are hard.

  If you take anything from this review let it be this: It is imperative that you start strong. Finish as many lectures and HW assignments as you can. You have a month for each of the first two projects (as far as I remember). Start them early. It only gets harder the more you advance in the course. If you want it to be easy, start early!!

  Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week

• qyZMWihEYt403LkFWtvfoA==December 8, 2025spring 2025

  Quantum Computing

  Pros:

  • Provides a good overview of quantum computing
  • Course is recent enough (2022?)
  • Textbook chosen by the course is a very thorough one; reading it will teach you rigorous understanding of basic quantum computing concepts.

  Cons:

  • Sorry but for the first half of the course, the lectures were nearly useless for me. The first half of the course was very math heavy, but the lectures do not go into nearly as much depth as necessary to understand the math. So for the first half, the course was just about reading the textbook and doing assignments.
  • The second half of the course was way too shallow; it goes over a few topics (routing, error correction, qaoa, etc.) but not to any sufficient depth. I totally coasted through this half of the course, spending <1hr per week, and felt like I learned very little.
  • The labs are very shallow as well. The code I had to write was way shorter than the instructions, and much of the time was spent on understanding what exactly the lab description wanted, because the specifications weren't precise. And because of that, I had to trial&error with the autograder to see if what I wrote conformed to the intended specs.
  • When they grade the problem sets and exams, they tell you what you got wrong, but they don't tell you the correct answer. I cannot understand why.
  • Problem sets and exams are all numeric answer or multiple-choice questions. There was not a single question that required a human to grade. The quality of the exam questions were poor - imo they did not accurately assess actual knowledge (btw I got above 90% on midterm so I'm not saying this because I'm salty or something.) I really think requiring some open-ended answers should be necessary to bring up the exam quality.
  • Despite the problem sets, labs, and exams being completely auto-grade-able, it takes weeks for them to be graded somehow.

  Overall, my recommendation is, if you want to learn quantum computing, just grab this course's recommended textbook, read through it carefully, do the exercises on it, you'll be good to go.

  Rating: 1 / 5Difficulty: 3 / 5Workload: 5 hours / week

• 62T6yVcGZM//iwKlfyyHUw==December 7, 2025summer 2025

  Network Security

  I can't recommend this course. While the intent behind using projects to teach is understandable, the execution feels needlessly time-consuming, and several exercises add little real value. Much of the material relies on outdated practices that don’t reflect current best standards, leaving students to fill in the gaps through their own research. To be fair, I did gain a lot from the course, but I could have achieved the same outcomes without the prolonged frustration of wrestling with the projects. In hindsight, the opportunity cost was significant; that time could’ve been better invested in more meaningful learning.

  Rating: 2 / 5Difficulty: 3 / 5Workload: 20 hours / week

• QNP+d4Ep9D5eP3lGGUGzvg==December 7, 2025spring 2025

  Artificial Intelligence Techniques for Robotics

  Finished with an A.

  Workload is not too crazy, exams are quite fair and they test closely to what is asked in the project and mirror the lectures (which are good). Projects are very fun! Take time to understand the lectures and then you will do well in the exam and the projects.

  Overall, one of the better courses and I strongly recommend it.

  4/5 because some lectures have errors, where you start doubting yourself and then you see at the bottom: "Actually, the lecturer did an error here and actually XXXX". It feels that rerecording some lessons and fix those errors would not take too much time.

  Also, it was not updated in a while; I am sure that there are now other methods. Also, we talked a lot of about sensor fusing, which is central to many applications but we did not have a project or a lecture about it, which went a bit more in-depth.

  Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week