NIGJZsk80tUgZnG5pO8bIQ==2025-12-10T13:51:11Zsummer 2025
Human-Computer InteractionGraded 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==2025-12-10T06:40:22Zsummer 2025
Machine LearningThis 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==2025-12-10T02:22:52Zsummer 2025
Deterministic OptimizationThis 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==2025-12-10T02:13:19Zsummer 2025
Statistical Modeling and Regression AnalysisI 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==2025-12-09T17:01:55Zspring 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==2025-12-09T01:09:18Zspring 2025
Introduction to Health InformaticsFall 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==2025-12-09T00:03:02Zspring 2025
Artificial Intelligence Techniques for RoboticsTook 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==2025-12-08T19:12:19Zsummer 2025
Artificial Intelligence Techniques for RoboticsI 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==2025-12-08T03:36:46Zspring 2025
Special Topics: Quantum ComputingPros:
Cons:
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==2025-12-07T18:57:30Zsummer 2025
Network SecurityI 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==2025-12-07T15:31:37Zspring 2025
Artificial Intelligence Techniques for RoboticsFinished 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
+aUPg94w7/ZsPuleoHS5hA==2025-12-06T22:18:40Zsummer 2025
Secure Computer SystemsNothing much to say about this abysmal course that hasn't already be stated below. Also I took it in the Fall of 2025. It is difficult because it is so poorly structured. This is hands down the worst and most worthless class I have ever taken in my entire life. Do not expect to learn anything useful or interesting. This class should be renamed to "Ancient computing history." The Professor should retire, he cannot speak or engage in any meaningful way throughout his lectures, frequently loses track of what he is speaking about, cannot be concise or well spoken, and cannot refrain from loudly exclaiming "SO UHHHHH" and "OKAAY?" every 15 seconds. Even watching at 2x speeds the lecture material is extremely infuriating. Each week there are 4-6 hours of lectures, which contains about 90 minutes of actual material because he speaks SO slowly and SO disjointed, constantly going off on tangents explaining the most menial and irrelevant things.
His quizzes are so poorly written he had to give everyone a point for one question because it was incoherent.
The midterm and final exams are atrocious. The poorly constructed and poorly written English will have you questioning your own ability at grasping the language as a native English speaker.
Avoid this at all costs. If you are in InfoSec it is unfortunately required. Georgia Tech needs to seriously re-evaluate this Professor and course. It has me deeply concerned for the rest of the quality of this program. I cannot believe I paid for this.
Rating: 1 / 5Difficulty: 5 / 5Workload: 10 hours / week
zIZzXHYQ2jFLtOKoXRineg==2025-12-06T06:03:55Zspring 2025
Artificial Intelligence Techniques for RoboticsTook this class in Fall 2025. This class had the best projects in the 4 classes I've taken so far. This is because they have a good balance of what they provide vs what they leave to you to figure out, plus it's very satisfying to finally succeed. There are plenty of resources between the lectures, tutorials, Ed posts, etc. to do well. However, this doesn't mean it's easy. There were a few times where I was really struggling trying to debug my project code (namely kalman filter, SLAM, and particle filter). Another thing I'll say is that this course is somewhat front-loaded, since the Problem Sets get finished about half-way through and there aren't many lectures after that. The only downside of this class is that the lectures can get pretty confusing at parts, but the staff does a good job at helping out when that happens.
Rating: 5 / 5Difficulty: 3 / 5Workload: 11 hours / week
zIZzXHYQ2jFLtOKoXRineg==2025-12-06T05:55:32Zspring 2025
Game Artificial IntelligenceActually took Fall 2025 This was a very fun course! I especially enjoyed the racecar and dodgeball projects. The lectures are super informative and applicable. And the projects are very reasonable. There were some topics covered in the lectures which could have made interesting projects, so I kind of wish there were more projects? Or maybe they replace some of the more boring projects like grid lattice? Anyways, I had a great time in this class and it was cool how enthusiastic students were about it.
Rating: 5 / 5Difficulty: 2 / 5Workload: 9 hours / week
RRm/hvoryLVrEKY1jIsQ+A==2025-12-05T19:32:13Zspring 2025
Introduction to Information SecurityDepending on your level of expertise will determine how your week goes. I had the hardest time with the binary exploration portion of the course. However, when the challenges dealt with web development and APIs I was able to crush it.
The discussion board was super helpful for hints from students. I would have probably failed without the hints provided by others. The office hours had gems as well.
Course is challenging, but super fun
Rating: 5 / 5Difficulty: 4 / 5Workload: 15 hours / week
fbJwRt1UE0TZLzwhqnu+ng==2025-12-02T23:56:04Zsummer 2025
Machine LearningTLDR: I put a lot of time into this class and got pretty consistently "good" scores (90 to 100+) on reports, but don't feel like that time translated into learning much about ML.
The Good:
The Bad:
Tips for future students:
Rating: 2 / 5Difficulty: 4 / 5Workload: 17 hours / week
VZuizvJ61QQ8b9TKcMFtTQ==2025-12-01T22:54:11Zspring 2025
AI, Ethics, and SocietyHonestly took this class as a break from the intensity of other ones. Was nice to have more of a social life for a semester.
As the other reviews called out, there's a strong focus on DEI and de-biasing datasets. It's absolutely something we should learn about, but it felt like the course just kept repeating itself. I left the class wanting more to learn about AI and ethics, especially in a world dominated by LLMs now. I will say I did like learning about the different strategies for fairness though
Realistically could spend 3 - 5 hours a week and slam everything out. The hardest part was actually remembering that I had assignments to do
Rating: 2 / 5Difficulty: 1 / 5Workload: 3 hours / week
rBAAprTd7n4xR3KjrheEEg==2025-11-28T19:47:26Zspring 2025
Artificial Intelligence Techniques for RoboticsOverall, RAIT is a very well organized course. As someone who's juggling a full time software engineer role and the OMSCS, this course helped me ease into the program. I wouldn't call this course a cakewalk, but extremely doable provided you put in effort.
======= Projects ======
Overall, the projects were super fun and a great way to apply the lecture material. Yes while the lecture material doesn't seem the most updated, I used these materials and the provided problem sets (the TAs provide the problem set solutions already) to get a good starting point for how to think of each of the projects above.
The exams also felt super fair. As long as you did the problem set diligently and understood how to implement the projects, you should be all good for the exam.
===== Course Staff ==== Honestly the TAs and Professor Summet made this course an awesome learning experience. In particular, John Liu's recorded sessions for each project were very energetic and gave really good hints for your project. Professor Summet is also very involved in the course and we see him in the Q&A office hours session. I recommend going to OH to get to know the course staff
====== Tips =====
As John Liu says, "don't get cute". Make sure you apply this advice! Trust me, if you keep your project code simple, you have less to debug and you may score better on the autograder. Write good helper functions/helper classes to keep your own code readable. Clear train of thought means clearer debugging for you
I found Kalman filters, Particle filters, and Warehouse search projects to be the simpler projects. These 3 projects are very doable provided you follow the course material closely (follow the videos in Canvas) and adapt the already provided problem set solution to the project. Tuning is the most annoying part, but if you go to the TA sessions, the TAs give you hints on "what ballpark you should be at for your constants"
Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week
0qNdCyeDuSTnuVthrbLt9g==2025-11-27T19:26:54Zsummer 2025
Network SecurityThe projects in this class are very old-school and take far too much time to set up. There were five major projects, weekly quizzes, and an exam, but the workload often felt unnecessarily complicated rather than educational. It is very difficult to understand the instructor, and the TAs are rarely available or willing to help. If you run into issues setting up VMware or dealing with the outdated tools, you’re essentially on your own.
The lectures don’t align well with the assignments, so you end up spending hours searching online just to complete basic tasks. Overall, the class feels disorganized, outdated, and overly stressful for the amount of actual learning you get out of it. This is one of the worst class I have taken, and it should be dropped overall.
Rating: 1 / 5Difficulty: 5 / 5Workload: 30 hours / week
qmDWKlaCJ4twB/BcvMjXcQ==2025-11-27T02:53:51Zspring 2025
Advanced Operating SystemsHardest course I've taken thus far of: GIOS, AOS, SAT, SDCC (yes, the one after this one. The hardest parts of SDCC are definitely harder than AOS, but the average week of AOS was much more than the average week of SDCC). The reason being it's just a ridiculous + consistent amount of content you need to be on top of if you want to maximize the material: multiple research papers per week, projects, lectures, and then tests.
Project 1 and 4 were hard, project 2 was not really hard but it was kinda tedious (so it will take some time), and then project 3 was easy, more like a primer for project 4. If you respect project 4 and really give it a go before the last week/2 weeks, you'll end up fine.
Rating: 4 / 5Difficulty: 4 / 5Workload: 20 hours / week
TreSFIHcK2mIcsyPqYBCsw==2025-11-22T04:33:07Zspring 2025
Introduction to Graduate AlgorithmsI’m writing a review for Spring but I retook this course in Fall and might have to take it a 3rd time. I’m not a bad student. I was summa cum laude in my undergrad and have kept a consistent A throughout OMSCS. This course is just the worst in terms of how it is executed. The material is great and I’ve learned a lot. But the 3 exams are 90% of the grade and the TAs will basically grade you depending on their mood. Life is already hard as it is, and I really hope the OMSCS facilitators for once look at the reviews of this course and have some mercy on the hard working students. In my recent exam I literally got a 4/20 in one of the essay questions because even though my solution was correct, I didn’t type it in exactly how the grader wanted. After spending hours of my life studying when I see the comments from the TAs, every moment just feels worthless. This course has given me true depression. I really really want to graduate and this is required for the Computer Systems specialization. If you’re early on in your OMSCS journey, just pick a different specialization. Don’t kill yourself mentally by taking this course.
Rating: 1 / 5Difficulty: 3 / 5Workload: 10 hours / week
11Wwcn5oDMuArd9QfJEb5w==2025-11-21T19:14:06Zspring 2025
Knowledge-Based AII found the lectures interesting but not terribly helpful. I really like the professor and admire the effort he puts in to provide a good learning experience and a great class though. The goal of the lectures is to teach first principles to use solving ARC AGI problems, but because there's not a lot of machine learning from data it feels less like an AI course and more like a few dozen leetcode problems. Some concepts were more advanced, like incremental concept learning (decision trees updated one data point at a time) but many AI concepts felt more like I was the only intelligence involved (represent problem with a state space, use guess and check to solve, etc). Most of the hours I spent on the class were on the semester long project, with took me around 100 hours. In retrospect I think it would have been faster and easier to ignore the course concepts and write a separate algorithm for each of the 48 types of puzzles than trying to build a general solver that uses course concepts like I did. The tests weren't fun but they felt pretty pair for other GT classes, and the rest of the mini projects were pretty quick. The course has a significant participation grade that required reviewing other peoples reports (required with each coding project). I wasn't a huge fan of that aspect, and felt like the reviews were not helpful, but they weren't too inconvenient.
Rating: 3 / 5Difficulty: 3 / 5Workload: 12 hours / week
11Wwcn5oDMuArd9QfJEb5w==2025-11-21T18:57:00Zspring 2025
Natural Language ProcessingI got more out of this course than most GT classes. The lectures were interesting and engaging, although several lectures were from partners with strong accents that significantly reduced my comprehension and learning. The course has a pretty light workload, with two tests, five homework assignments, and weekly reading quizzes. I only have two real complaints. The homework instructions are fairly poor and unmaintained. Every assignment had several points of confusion because the assignment had very strict implementation requirements, but vague, unhelpful, incorrect, or actively misleading instructions. Many common points of confusion persist across multiple semesters without updates from the staff. My other complaint is that the 2 point weekly reading quizzes are proctored with a room scan, which always feels like an unnecessarily silly hassle.
Rating: 4 / 5Difficulty: 2 / 5Workload: 4 hours / week
lw7U0vvJN+5npC93uffExw==2025-11-21T16:09:40Zspring 2025
Artificial IntelligenceThe lectures for this class teach a lot, but seem detached from the content on the homework assignments. Although some students seem to excel in this class, I think it is very realistic to expect to invest around 25 hrs/week between lectures and homework assignments. The TAs for this class also act quite passive-aggressively, although I am aware that that is nothing new for Georgia Tech TAs. Assignments and exams word question in the most ambiguous manner solely for the purpose of making them more difficult, regardless of what the professor and TAs may claim.
Rating: 2 / 5Difficulty: 4 / 5Workload: 25 hours / week
ndcxjsoHRD8P0uT+Y5VKOA==2025-11-19T10:00:27Zspring 2025
Special Topics: Applied Natural Language ProcessingWhile the subject is cool, the course itself is a bit disappointing. The assignments give almost too much starter code, so it ends up being "mess with dimensions of neural networks until the code runs and Gradescope is happy". Quite a missed opportunity to force a deeper understanding of the material.
Rating: 3 / 5Difficulty: 2 / 5Workload: 10 hours / week
ndcxjsoHRD8P0uT+Y5VKOA==2025-11-19T09:57:48Zfall 2024
Computing for Data Analysis: Methods and ToolsDecent "intro to programming" course. Not that difficult if you already have a background in Python. I didn't know SQL from before, but easy to pick it up during the course. Just make sure you understand the concepts well enough that most things come to you "automatically" during the (timed) exams.
Rating: 5 / 5Difficulty: 3 / 5Workload: 8 hours / week
JSYEBKrCp/OkgYNve7bHkw==2025-11-19T07:52:53Zspring 2025
Introduction to Information SecurityThis review is for Fall 2025, which isn't currently a selectable option.
Background:
Time: No lectures, no quizzes or exams. Just CTF-style mini projects.
Man-in-the-Middle: 7h, 104/100; wireshark and knowledge of IP/TCP; fun intro. Feel like a detective.
ML: 20h, 92.5/100; first time with pandas, numpy, sklearn. didn't want to tune params for last points. unspectacular. was just a "hello world" type intro to these libraries for a cybersec-related data set.
Binary Exploit: 22h, 100/100; very fun and rewarding. no real exposure to ASM before this; did not attempt EC. The overflow attacks and ROP chain attacks were cool to learn about. Even though I'll likely never use in industry and mat'l is pretty niche, this was probably the funnest project in the course, and I was tempted to take the BinExp lab class.
API: 7h, 100/100
Web: 10h, 100/100; learned some XSS and CSRF
Log4j: 14h, 100/100; did spend 4h half-heartedly trying the EC
Malware: 6h, 89/100; only tried 1 runthrough for the T/F Joe Sandbox Reports and didn't want to bother
Cryptography: 13h, 92.5/100; python scripting for crypto related stuff. Vigenere cipher code was kinda fun. Working with really large numbers for RSA was interesting. Got bored at end
DB: 7h, 42.5/100; just did what I needed to squeak out an A in the course. Skipped some of the inference problems to work on SQL injection
Experience: I enjoyed the course, mainly because I am avoiding courses that are exam-heavy or have a group project component.
The professor was nowhere to be found. In other courses, they'll usually even make a single intro announcement at the beginning of the course, or have pre-recorded, decade-old lectures to watch. Nada from Lee. Entirely TA run.
The TAs were excellent, managed the Ed discussion board well, responded very quickly to questions, and tried their best to give hints while not giving away too much.
Overall, class feels more MOOC-y, as there were no lectures, and assignments just had some links for reading up on the subject for that project, but these were usually pretty good references.
Class has definitely changed since Summer 2024, where more projects were added. Only wish all projects could just be released all at once at the start.
Rating: 4 / 5Difficulty: 2 / 5Workload: 7 hours / week
V1B7qR9vriLJbflXMnn+Vg==2025-11-17T21:05:12Zsummer 2025
Introduction to Analytics ModelingI wanted to share some honest feedback after taking two midterms of ISYE 6501. I’ve really enjoyed the lectures and learned a lot from the assignments — even though they’re quite time-consuming, they’ve been deeply educational and practical. But I feel that the exam setup and grading structure don’t really align with the spirit or learning goals of the course.
To start with, the exam questions feel misleading and unevenly weighted. Some questions are worth 5–6 points while others are only 1 point, and it doesn’t seem that this distribution was carefully reviewed. In many cases, it felt like the challenge was not about applying what we learned, but about trying to interpret what the question writer was actually asking. That creates unnecessary stress and confusion, unrelated to the actual course material.
I can’t help but wonder — are the exams thoroughly reviewed by Prof before release? Because the quality of the lectures is excellent, but the exam feels disconnected from that level of care.
Also, I think the grading structure overall needs reconsideration. Homework only counts for 15% of the grade, yet it easily represents 60–70% of the actual learning in this course. I understand it might be weighted lower because it’s peer-graded and perhaps not taken as seriously in scoring, but that’s exactly the problem — it’s where most of the effort and meaningful understanding happen. If that’s the case, then the peer-grading system itself should be reevaluated, not just for the sake of TA workload, but to ensure that students are fairly rewarded for the depth of learning these assignments provide.
Meanwhile, exams make up 75% of the grade, even though they often don’t reflect how well someone understands the material — just how well they can navigate ambiguous or oddly worded questions.
I truly think ISYE 6501 is a great course with strong fundamentals, but it could be so much better if the exams and grade weighting were revisited to better reflect the effort and real learning happening throughout the semester.
Rating: 3 / 5Difficulty: 3 / 5Workload: 10 hours / week
PfmIAfgIvMdo9V5jd4mYPQ==2025-11-17T14:18:47Zsummer 2025
AI, Ethics, and SocietyTook this fall 2025 but had to choose summer 2025 since there's no fall 2025 option on here yet. This class is tedious busy work, repeating the same stuff in every single homework, response etc. If you have the patience and want an A and don't care about learning anything, take this class.
Rating: 1 / 5Difficulty: 1 / 5Workload: 10 hours / week
vEDR52mr/uZsH35uONsC0A==2025-11-17T04:45:27Zspring 2025
Machine Learning for TradingReally enjoyed this as my first OMSCS course. The coding parts were not insanely difficult, but the projects felt rewarding and it was nice to see them building on from each other after each assignment.
The part that surprised me was all the report writing, be prepared for multiple multi-page reports that take time, require clear citations, and research.
Overall, though, definitely enjoyed this course; it served as a great introduction to machine learning for trading.
Rating: 4 / 5Difficulty: 3 / 5Workload: 20 hours / week
uUUuQTRF6I6RIgjLAcwjnA==2025-11-15T16:32:34Zspring 2025
Data and Visual AnalyticsThis class is nothing short of a disaster. On paper, this could be a blockbuster class in the program. Visualization is such a critical piece in the field of data science. Telling a story with data is a cornerstone skill in the profession. Often times, its more important than the analysis itself. Conveying insights can be so challenging with numbers. Visuals can piece layers of complex understanding with imagery that sells an analysis on its own. Some of the most compelling projects in my career have been propelled because an executive remembered a visual we created.
This class promotes none of those critical skills. Lets step through what the semester looked like:
Lectures: The lectures had no relevance to the class. They were often vague, scattered, and completely disjoint from the class itself. You could get through the class with an A without tuning into a single lecture. That is not learning. I'm sure Polo is a qualified professor, but his delivery in these lectures was disappointing.
Teaching Assistants: I did not engage personally with the TAs, but I observed many interactions with the TAs on the Ed forums. Quite frankly, many of the TAs are extremely arrogant and condescending. There was some confusion around technology in my section, and the TA's response was 'read the syllabus/instructions' and 'its not our problem if you can't find the answer'. The questions being asked had nothing to do with either of those things. Additionally, one of the technologies required to be used went down for a period of time. It was required for the homework. Rather than allowing a grace period for the downtime, the lead TA simply said 'not his problem' and many students were left with a 0 for that portion. For this class to get better, this has to stop. I can go on and on with more examples of this type of behavior. If this slate of TAs is unable to change, then they need to be replaced.
Content: There was very little content relevant to the world of data visualization in this class. The first assignment was some strange actor graph chart coupled with some other obscure analytics that had no relevance to visualization. The second was the infamous D3 assignment that is irrelevant. I think half the class just didn't do it because its not necessary for an A. The third assignment was doing the same analyses in like 5 different data platforms. The fourth was coding up a random forest from scratch. If you had trouble at all, these are all things that a Saturday morning, ChatGPT, and cup of coffee could figure out before lunch time.
The project was a total joke. In our peer grading, its very obvious that very few groups took this seriously. One team submitted what was very obviously a ChatGPT-designed poster. The project is worth 50% of your grade, to see so little effort was disappointing. The design of the project was also confusing. Why the emphasis on research literature? We a 6-person group, which meant we needed to submit 18 peer reviewed articles summarizing all of them + complete a project proposal within a 2 page limit. Thats just frankly unacceptable. Its not possible to generate a robust proposal when Im given less than a page to work with after the literature survey is complete. There was also an idyllic obsession with 'novelty' in the project that is just frankly absurd. Who cares? The point of this class is to develop skills, conduct complex analysis, and be able to show it in a way that a casual observer can understand. The focus on 'finding the next best academic thing to improve on' just propagates the idea that this profession is out of reach of the most casual observer. Its not.
None of these things had any relevance to data visualization. Which chart should I use for what purpose? How do I use color shading to draw attention to important things? What are the best ways to build a presentation to tell a story about data? How do I layout a slide in a presentation for maximum impact? These are things you'd expect out of a data visualization class and they just simply are not taught here. Why are we coding random forests in a visualization class?
You'll have to take this class, so there's no getting around. Start homework early (you get ~ 3 weeks), make sure you have a decent project group, and just recognize its going to be a slog.
Rating: 1 / 5Difficulty: 4 / 5Workload: 15 hours / week
39cf0jJMIGBoygwgu63dEA==2025-11-11T21:06:01Zsummer 2025
Special Topics: Business Fundamentals for AnalyticsI actually enjoyed this course because I had never taken a business course before and it was good to get an 8-week overview of Accounting, Finance, and Supply Chain (oh yeah and Marketing was meh). My background is in applied math, computer science, and data. I can see why people feel like it's a wasted course in an analytics program though.
Your grade comes from 4 exams (I guess 5 in the fall/spring?) and 2 simulations. The exams require mostly memorization but it's not too bad. Accounting and Finance had some challenging problems but then it was pretty easy after that. The simulations don't feel that connected to the material. It felt like you could figure out what you needed to do through trial and error (you get like 10 simulation attempts) rather than by using a specific formula from the class. Overall I felt like I learned a lot even if I wasn't training any ML models.
Tip: Watch and practice alongside any Excel demo videos that a TA offers as they can really help work through the multi-step Finance problems.
Rating: 3 / 5Difficulty: 2 / 5Workload: 3 hours / week
39cf0jJMIGBoygwgu63dEA==2025-11-11T20:47:28Zspring 2025
Data Analytics in Business(Actual workload: 15 minutes / week) The hardest part about this class is remembering that you're enrolled in it. It kind of feels like just a way softer version of ISYE-6501. If you have any background with programming and data, and I might even include taking ISYE-6501 in that, then I would recommend pairing this course with another unless you want to do a bunch of extra self-learning as well. My wife and I had a baby during the semester that I took it alone so it worked out perfect for me.
Your grade is 100% homework, and the homework code is essentially handed to you. I spent about 30 minutes on each homework and assignments are every other week. Of course if you're not familiar with R or the models in question then you'll need to spend more time, and if you don't have a newborn then you'll probably feel the obligation to do some extra self-learning. Either way, use this course however you need to cause I don't think it gets easier than this.
Rating: 3 / 5Difficulty: 1 / 5Workload: 1 hours / week
39cf0jJMIGBoygwgu63dEA==2025-11-11T20:33:05Zfall 2024
Introduction to Analytics ModelingFantastic introduction to the program that provides a broad survey of analytical methods. The difficulty will be mostly dependent on your background with coding, R programming, and data analytics in general. Dr. Sokol's videos are fun and easy to follow. The office hours go over homework problems. It was still a great refresher course even with years of coding and data experience. I would highly recommend taking this as your first course!
Rating: 5 / 5Difficulty: 2 / 5Workload: 8 hours / week
8sAhLR5isWyLXfXU2S/kHA==2025-11-06T11:51:53Zsummer 2025
Computer NetworksComprehensive and informative course, nice break in the summer from heavy coding courses s.a. RL/ML/DL etc.
I had zero knowledge in CN and genuinly found this interesting.
Well structured, can rely 100% on the course material. Bit long at times, I reckon more experienced folks can be bored and just skim through.
Tests are doable if you properly train on the quizzes.
Recommend.
Rating: 5 / 5Difficulty: 1 / 5Workload: 3 hours / week
0sKRpcztNb2FelynZvAucw==2025-11-02T23:11:33Zspring 2025
Introduction to Cognitive ScienceI took this class because I needed an easy class this semester due to life being busy, and the reviews looked good, but I honestly kind of regret it. I am not engaged and I don't enjoy what we are covering. Feels too meta and too overanalyzed.
Rating: 2 / 5Difficulty: 2 / 5Workload: 10 hours / week
QXVJyKsMsSwQ8RZ4khjCCw==2025-11-02T08:33:01Zsummer 2025
Machine Learning for TradingTook it summer 2025, there was a lot of changes from before. Made the class much harder. Read the other review below on summer 2025, I pretty much agree with the person. Project 3 and Project 8 was the worse, spent about 40 hours a week on both, with project 3 taking up to 5am on a Monday. You can lose 30% of your grade on coloring a line wrong.
Just my comparison, I took GIOS which was supposed to be a much harder class and found it very rewarding and got a solid A in. I spent about a similar amount of time in ML4T and barely squeaked by with an A. I found ML4T more stressful and harder than GIOS in several ways, which is a testament to how much it has changed and why the earlier reviews are outdated. Plus they changed the open book exam to closed book with no calculators involved, so there's that...
Worst part was how they didn't release grades until super late. The only reason why I give this course a 2/5 instead of a 1/5 is because they at least released all grades before the drop deadline. However, I had gotten an F on the first assignment causing my grade to be a D for over 1 month, and was just about to drop until I realized I did well on the other assignments. Overall, huge amount of anxiety caused by the grading and how it's released so late. Like I said, you can lose 30% of your grade because the color of your lines are wrong or you labelled your graph title the wrong way.
Rating: 2 / 5Difficulty: 5 / 5Workload: 30 hours / week
o6S8rstKct8Vt6oBmGAUKA==2025-10-30T14:07:21Zsummer 2025
Introduction to Graduate AlgorithmsI agree with the previous review in that the a single FRQ on the exam is weighted far too much. I feel that I have a strong command of the material, especially for the most recent graph exam. One of the FRQ was trivial to solve, the other was challenging for most students including me. Of course, it will ultimately be the burden of the students when a sizeable chunk fail. I believe that I could have likely solved another FRQ with ease, but instead will likely have to retake the class, wasting my time and money, because I could not solve this question in time and kept doubting that I was covering all edge cases. In short, find a better way to evaluate students than a handful of FRQ. At least the TAs seem to care, and I applaud Joves and Brito for holding regular office hours.
Rating: 2 / 5Difficulty: 2 / 5Workload: 10 hours / week
SvXQpVY+HCF5y5XhAw2RHw==2025-10-28T21:01:37Zsummer 2025
Introduction to Graduate AlgorithmsI just hate the exam format, the pressure is really intense, and can make you freeze up. I don't think it really determines your knowledge of the class. Good material, but horrible execution. Such bad execution of the exams that if I could give it a negative rating I would.
Rating: 1 / 5Difficulty: 4 / 5Workload: 25 hours / week
cqkszj3/U5ArMQCkrnHLzQ==2025-10-28T02:27:22Zspring 2025
Information Security Lab: Binary ExploitationThe course teaches you a lot about how Linux executables work. You will learn how to do ROP and other cool techniques to overcome mitigations for memory vulnerabilities.
Rating: 5 / 5Difficulty: 5 / 5Workload: 15 hours / week
Ltwx2luOLN5F805ytBZZPA==2025-10-22T03:55:19Zspring 2025
Introduction to Analytics ModelingThis class is a good introduction to analytic models. The homework can be tedious if you are unfamiliar with R. Most of my time for this class was spent on the homework. The lectures are well made and easy to follow. The exams, while much harder than the practice quiz, are fair. They tend to test you on your understanding of the concepts, thus no memorization is required. You are also allowed cheat sheets for the exams, which helps solidify your understanding.
Rating: 5 / 5Difficulty: 2 / 5Workload: 10 hours / week
vbJgUwTFjnDzV/sxUH//3g==2025-10-12T17:10:12Zsummer 2025
Artificial IntelligenceProbably the best course in the AI/ML tracks, by a long shot.
The projects in this course are fantastic. The project which stuck with me the most was the Decision Tree assignment. The Decision Tree assignment has you building the entire algorithm from the ground up, calculating each piece of the process manually using matrices and mathematical functions. It blew me away, and it really made me think that this is how the ML course should've been structured.
The exams were tough but fair. There were no surprises and the course materials were sufficient preparation.
The lectures were engaging but not 100% necessary for exam prep. You can play most of these a 1.5x-2x speed, but normal speed when they start describing algorithms.
I took ML and dropped it because it sucked. If the ML course were ever to be redone, I'd hope they'd break it out into two separate courses (Supervised ML and Unsupervised ML) and the assignments would go into the level of technical depth that the AI Decision Tree assignment had. Being able to write a convincing paper is a necessary skill, but it shouldn't be the only takeaway from the only ML course in the program. AI goes deep, and it's the reason why it's my favorite of the 4 courses I've taken so far (CN, GIOS, NetSci)
Rating: 5 / 5Difficulty: 4 / 5Workload: 15 hours / week
Fk0FQSMCiLF4JSvmuaGBhg==2025-10-01T05:27:43Zsummer 2025
Introduction to Computer VisionThere was a lack of clear expectations, rubric was not giving meaning that reports often were heavily penalized for not including details despite lack of clarity in what should be included.
Rating: 2 / 5Difficulty: 5 / 5Workload: 8 hours / week
4YpbCjbYv2B3IvKU2aXZhg==2025-09-25T09:08:07Zsummer 2025
Introduction to Analytics ModelingI had mixed feelings about this course.
On the positive side, I found the course content and rigour to be a solid way to kickstart my OMSA journey. The professor is super engaging and enthusiastic, and his lectures explain both the theory and examples very well. That said, the homework is largely self-directed - you’re expected to code independently (+ learn to code for those with no R background). The first homework was by far the most challenging for me (and possibly for other students too), which caught me off guard. After that, the remaining assignments were more manageable. Just note that there are one or two of the later weeks that need Python, which can be tricky if you don’t have prior experience.
Peer grading was frustrating at times since it’s inherently subjective - often the difference between a 90% and 100% depends on the grader, and many seemed to approach it as a chore rather than a peer learning opportunity. Personally, I would have preferred more (or any) TA grading, though given that homework makes up only a small portion of the final grade, it’s hard to complain much. There’s also a mini project, but it’s fairly light - no coding required, just a short report of your problem/use cases and proposed analytics.
The quizzes are what really determine your final grade. As long as you keep up with the lectures, you should be fine, though some of the questions are worded in a tricky way. Unfortunately, the quizzes don’t tie in with the homework at all, which further adds to my impression that the course workload feels disjointed.
TLDR: Great professor and lectures, but homework doesn't really align with the effort required (so don’t stress over it too much).
Rating: 3 / 5Difficulty: 3 / 5Workload: 10 hours / week
4YpbCjbYv2B3IvKU2aXZhg==2025-09-25T08:51:20Zsummer 2025
Data Analytics in BusinessI was initially hesitant about taking this course because of the negative feedback on its lack of structure and usefulness, but I also could not run away from it since it's a compulsory module.
That said, I was fortunate enough to take the revamped version, and I’d say I actually enjoyed it. The good thing is that the weekly homework is essentially a direct 'copy and paste' of the lecture exercises, with a few adjustments to the data and variables. As long as you grasp the lecture content, you’ll do fine. This also becomes a bad thing, as the course does become a little too straightforward for graduate-level.
Still, as one of my first two modules in OMSA, it was a good way to ease back into studying without being overwhelmed by coding and troubleshooting on my own. Taken alongside ISYE6501, this course felt like a simpler, more structured version, though there was definitely room for the homework to be more challenging/stimulating.
TLDR: Engaging, straightforward, and an easy “A". Seems much better than the previous version of MGT6203.
Rating: 4 / 5Difficulty: 1 / 5Workload: 3 hours / week
ZeM0UXNY1NJrZY00h0Ehzw==2025-09-23T16:53:03Zsummer 2025
Network SecurityEnded with A. The content of the class isn't too difficult. The exams were extremely easy - I stopped watching lectures 1 week in because of how bad they were, instead reading notes I found from past students. I don't think any of the assignments are hard. However, most assignments are ambiguous in what you need to do and are tedious. Would recommend taking it in the summer, pretty light course load, went on multiple week long vacations during the class.
My two main problems with the course were:
Rating: 2 / 5Difficulty: 2 / 5Workload: 8 hours / week
S4qbEenZksl0HwqhSf0WrQ==2025-09-16T03:47:49Zspring 2025
Machine LearningI'm only stuck on the first project and already about to drop this class. The problem - they do not teach you ANYTHING that you need to know. The course pre-requisites say that as long as you've taken and "introductory course in artificial intelligence" class you'll be okay. That is 100% a lie. Do not take this class unless you ALREADY KNOW everything about ML, because they will NOT teach you.
They will throw TONS and TONS and TONS of concepts at you on the first project and not explain them at all in any way. You're just expected to google everything and learn it. It's clear that the assignment was written with the expectation of somebody who's already used scikit-learn to make multiple ML models before and is intimately familiar with it, NOT just "someone who's taken an introductory course in Artificial Intelligence" like the course webpage says.
the lecture - "heres the basics of how decision trees, knn, svm, and neural nets work"
the assignment - "you're expected to already know how to stratify data, you're expected to already know how to cross validate data. you're expected to know terms like ROC-AUC, PR-AUC, prevalence baseline, MAE, MedAE, MSE, MAPE, SGD, confusion matrix, etc etc etc. (And those are just the terms i see on ONE PAGE of a 16 page document). You're expected to already be intimately familiar with scikit-learn, because the directions will make reference to implicitly, so if you don't already know it you'll just have no clue that it's even referencing sci-kit learn. You're expected to already know a ton of data science concepts because the instructions will make reference to them without explaining them. If you deviate from these instructions in any way you will be docked 50% of the points on this project. Furthermore, these instructions will be self-contradictory unless you're already intimately familiar enough with ML and data science to be able to discern the true intent of the instructions."
Don't even bother watching the lectures, they are a complete waste of time for the purpose of the project. The project assumes that you're already a master expert of ML, the lectures only cover the very basics.
The only way to survive is to have ChatGPT open and just ask it to explain everything to you, bit by bit, piece by piece. ChatGPT will teach you how to complete the assignment because the instructors won't. Hope you're got the premium subscription. Even then, it's nearly impossible.
I can't even comment on the quizzes, the tests, or the grading, because I can't even get there yet.
TL;DR do NOT take this as your first ML course in OMSCS despite what the course pre-reqs say. It's clear you should take it as your LAST.
Rating: 1 / 5Difficulty: 5 / 5Workload: 60 hours / week
Eucmhp9VNtNHzZK5/sDYfw==2025-09-06T08:07:57Zsummer 2025
Computer NetworksIt was a nice and informative class on computer networks. Highly recommend that you don’t take this during the summer like I did, the projects can be a bit difficult if you’re not really too proficient with Python. I think I would of rated it higher if I took it in spring or fall.
Rating: 3 / 5Difficulty: 5 / 5Workload: 15 hours / week
R/mDYLrlsAd4LJEtZJWniw==2025-09-03T13:27:20Zsummer 2025
Software Development ProcessAssignment 6 man. I was on track to get through this class with ease but then got hit with the 20% on this assignment and ended up with a C...
TAs were okay, group project was okay, individual project was actually kind of fun.
Please take assignment 6 seriously.
Rating: 2 / 5Difficulty: 3 / 5Workload: 8 hours / week
9BTN/KCHgyi5KsCx7q4SKsFJPu8fv5zAo/LMa3Dho8M=2025-09-01T05:36:45Zsummer 2025
Statistical Modeling and Regression AnalysisThe content is not difficult but the coding homework is brutally time consuming. Often there are 15 problems with each problem having 3 sub problems. The timed, proctored, closed internet coding exam was also brutal. I'm someone who usually finishes timed proctored exam in less than half of allotted time, but I used the full 4 hours to the last minute for the coding exam in this class.
I know it's doable in Python, but it's definitely easier with R. TAs officially "strongly recommend" (in their own words) using R.
The professor is mostly absent. Assignment instruction is often confusing and frustrating as you constantly have to chase for clarification.
Rating: 1 / 5Difficulty: 5 / 5Workload: 18 hours / week
CNFJRTK8LziCE4STdYeT/Q==2025-08-30T06:37:52Zsummer 2025
Introduction to Cognitive ScienceThis class is incredibly easy and most can finish with a solid A if you put in some effort. It's also an excellent work-ahead class and is ideal if you foresee a busy period of your life coming up or simply want to pair with a more difficult class. Although I would avoid pairing this with a report-heavy class like ML as the final report legitimately requires a significant amount of time.
Quizzes (15%): The quizzes are open-book and you have two attempts. They are heavily based on the lectures and associated readings and you can easily figure out the answers from skimming the readings. The quizzes are also open from day one and can be knocked out in a few hours.
Individual exercises (35%): For summer we had 6 individual exercises in which you are presented with readings and questions to answer. The requirements are pretty clear and a slightly deeper reading of the literature will give you everything you need to know. I'd say just give them what they're asking for as clearly and succinctly as possible.
Term project (45%): The term project is the hardest aspect of this class and honestly the most time consuming. The project has four milestones in which you pitch your project, submit the final project, and prepare a presentation on the project. There is an optional milestone that you can submit if you want further feedback early into your project. The project itself is an individual effort and you have options of writing a literature review, performing a cognitive science experiment, or creating a computational model. I would say START EARLY and budget more time than what the course requires (~100 hours). I had to take a couple days off work before the due date to bring this one to the finish line. I would also say to put some serious thought about what project you want to do early on as pivoting halfway will seriously put you behind.
There are no real downsides of this course except for the fact that the TAs can be a bit unresponsive at times. However, the grading is VERY generous so you really can't complain. If you address all of the points for the exercises and term paper clearly, then you will get an A.
Overall, what you put in is what you get out, but I honestly feel that putting in a significant amount of effort is not rewarded as equally as just doing the bare minimum. I will say that the course content itself is interesting and very thought-provoking, but again, the class itself is not challenging and it leaves plenty of space for you to take the initiative if you want to dive deeper into the material.
Rating: 4 / 5Difficulty: 1 / 5Workload: 10 hours / week
yS5aZpZ4oKps04hQR6K/lw==2025-08-25T13:39:54Zsummer 2025
Introduction to Graduate AlgorithmsI took this class over summer full time and it was brutal, I passed with a B, but it was not an easy B and there were a lot of times during the entire semester I was worried I wouldn't make it and considered dropping but I refused because I needed this course to graduate unfortunately.
The difficulty in this course doesn't come from the material but in the structure of the class and how it is run. I actually really like algorithms but this class was more like a writing class on how to write pseudo-code as prose.
Homeworks are supposed to provide guidance for exams but the homework feedback is provided ultra late like a few days before each exam when you turned it in weeks before. This wouldn't be so bad if the exams were curved or not worth the entire grade, but yea that's just how this class is structured to be high stakes and stressful for no reason in my opinion.
Oh yea, and the notes are all closed notes and closed book, again adding artificial difficulty like giving a really hard math problem to solve by hand and saying no calculators. Yea, it's hard especially when you need to learn how to solve so many different cases in such a short period of time but it's also not really valuable or useful.
I can't really provide much advice other than to focus on completing the lectures, do the practice problems, and focus especially on the homework problems because you're very likely to get a variation of a homework problem or lecture problem for your exam moreso than a practice problem (Although they do still show up and that's why it's such a huge time sink, you will cover around 20 different problems, but only get asked 2-3 on every exam for the free response which is worth more than half the exam).
The TA office hours (Jove Luo) were really helpful. I feel bad for the cohort that has to take the class if he decides to take a break because the OH provided me so much help when I felt lost.
He covers the practice problems and gives numerous examples of the desired answer format, it was very helpful to me, but really the most frustrating thing about this class is that you really don't get enough opportunity to practice your own solution writing and getting feedback until basically the day the test arrives so that's why so many people bomb the exam, it's not hard, but it is when you have had 0 time to actually practice by getting feedback from the staff and not living on an island.
The worst part is that they're very strict with the point removal, but also make a lot of mistakes. I made 2 regrade requests and won both, 1 regrade removed 20% of my entire exam grade for a mistake the TA made and then they returned it back 100% admitting the error, but that entire process of seeing my bombed test score, then reviewing the notes, having to write for feedback, then write a regrade request was just all unnecessary work that was caused twice for me.
Sadly, this is a required course for a lot of specializations so there's no avoiding it, but hopefully this info is somewhat useful for where to focus and a strategy to pass because it would have saved me a lot of time, instead I spent so much time trying to cover everything and it really was an unnecessarily stressful experience to an otherwise well-run program for professionals.
Rating: 1 / 5Difficulty: 5 / 5Workload: 40 hours / week
Mg8VbB4wqnj8Dv27PhxvHQ==2025-08-25T00:42:27Zsummer 2025
Game Artificial IntelligenceA little about me: I have a bachelor’s degree in CS and about 1.5 years of SWE experience.
Courses I took before this one: IIS, SDP, and VGD.
I think this course is overall good and pretty manageable in the summer. The grades are basically made up of 8 different projects (and quizzes). You don’t really need to take VGD before this course either.
It’s the kind of class where you get out what you put in, I think some people can get by with ~5 hours a week, others might put in closer to 20 depending on how much effort you want to invest.
The only frustrating part for me was that you only get 2 submission attempts per assignment on Gradescope, but other than that, this course is great, definitely recommend.
Rating: 5 / 5Difficulty: 2 / 5Workload: 7 hours / week
4bAmToXB3t9tFYKjhRjd0Q==2025-08-23T23:43:22Zsummer 2025
Digital MarketingStudy for the midterm and final. Doing bad on those are the only way you could get anything lower than an A.
Rating: 5 / 5Difficulty: 1 / 5Workload: 5 hours / week
R1Kj70Cla4uAqu/Wj9AR4Q==2025-08-23T13:46:37Zsummer 2025
Introduction to Information SecurityLet me start with my TLDR: Its a fun course to learn surface level security knowledge. Be suspect of the difficulty and time commitments it currently shows. The class used to be much easier and less rigorous in the past. Advice: START THE PROJECTS EARLY!
More in depth:
This is my second course in the OMSCS program, with the first being ML4T. I also work full time as a Data Engineer.
I imagine that when the TA's put together the course content, they did so with the vision of 'if i was training a jr cyber analyst, what should they know?'. If you are interested in learning that material than you will enjoy the content and projects.
Overall I would say that this class is not 'easy' but 'straightforward'. Its 100% project based, so if you put in the effort you should get an A. This is a class with a wide breadth of topics, that are totally unrelated to each other. So, you can spend 8 hours on the API project but 50 on the binary exploitation project (yes, that was me). I would assume that for at least one project, you will have knowledge gaps and have to spend more time than you think.
How it worked (for summer 25): Projects would open and you would have until Sunday midnight est to complete them. Usually the next project would open on Friday so if you got ahead of the game, you can have up to 9 days to finish each project. If you are not confident in binary exploitation, I would HIGHLY recommend working hard to make sure you have the 9 days for this project.
What I liked:
You will use a decent amount of tools, languages and techniques that will assist you in a jr. software/security career.
Finding the flags is a nice dopamine rush :)
TA's really go above and beyond in creating some helpful tutorials and relevant sources for research.
All the projects are 'do-able'. I really had no assembly and computer architecture knowledge. But I was able to finish the binary exploitation project with an A. But to do so, I had to do a crash course in all of the above, along with the C debugger and was able to complete the project. Again, its all about the effort/time you put in.
Once you finished your project, you were 'done'. Nothing else to worry about until the next project opens.
What I didn't like:
No relevant lecture content provided by the professor. I think that relying on the TA's for 100% of the content is shameful. For a supposed expert in the field, why is there no learning content for the projects? It seems very lazy and it's obvious the professor does not care at all about this class. Relevant lectures/overview on the topic for each project should be a bare MINIMUM for a professor. This would GREATLY improve the learning experience. I left the class getting an A, but honestly if you don't take diligent notes during your research into the topic and review them, its very easy to just brute force projects and not learn anything.
I also think it is a huge waste that the database security project was 'last' because it was one of the better projects in my opinion.
Time spent:
For average hours per week I calculated the time from the day the semester started to the end, so there was 2 weeks where we didn't have any work because there was no final.
I put together a list of time projects took me to get an A on all of them. Keep in mind, i'm a slow worker and liked to take my time to go in depth on the topics taking notes to learn more. If just 'getting the A' is your goal, you can probably cut these times in half because you can definitely game this class.
(Man in the Middle): 30 hours (ML): 20 hours (Binary Exploitation): ~50 hours (API Security): 8 hours (Web Security): 20 hours (Log4Shell): 25 hours (Database Security): 20 hours (Cryptography): 20 hours (Malware Analysis): 15 hours
Rating: 3 / 5Difficulty: 3 / 5Workload: 18 hours / week
+vrBbYGMp/QLf7FttbIhIw==2025-08-23T06:25:03Zsummer 2025
Knowledge-Based AII enjoyed this course. It helped me frame my perspective about AI Agents. The foundations on how an AI Agent need to be built is interesting. Newly introduced ARC AGI is fun. Summer was little short in duration to do ARC AGI project in truly KBAI manner. However, I feel I have broadened my thinking and knowledge in this field after this course.
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
+vrBbYGMp/QLf7FttbIhIw==2025-08-23T05:55:20Zspring 2025
Machine Learning for TradingI took ML4T as my first course in OMSCS. After an academic gap of 9 years, I felt this course helped me slide smoothly into academics. This course though some find it easy, I believe is well organised with excellent TA Support. I would suggest keep reading all posts comments on Ed discussion. If you have any doubt, it can be put on Ed and TA almost immediately responds which is really helpful. Weekly TA live discussions are excellent as this being an online course, it is quite challenging to get doubts clarified so even if you cannot attend the live, recording will be available within a day. Even the questions put on Ed will also be discussed in those sessions. Though project descriptions are extremely lengthy, TA goes through it fully in those sessions.
Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week
nDdwarmsB6c18RBejfPSgw==2025-08-23T03:06:26Zsummer 2025
Artificial Intelligence Techniques for RoboticsCourse was well structured and the syllabus was rational - you learn about robot localisation, path finding, PID, and SLAM (alongside other smaller topics). The projects were effective in forcing students to actually understand and implement these concepts, although the difficulty gap between the lectures (high level) and starting on the project can be quite big.
Exams were closed book, proctored MCQs which were challenging but fair. Other than these 2 major components, there's almost no busywork unlike other courses which was refreshing too.
Overall, good course to take during summer that won't kill you, but also challenging enough to feel like you did learn something useful by the end of it.
Rating: 4 / 5Difficulty: 4 / 5Workload: 14 hours / week
RadUHmNAM2Eel0fvwEKV6ClfqQzavWZESRkwywSXU/0=2025-08-22T00:26:08Zsummer 2025
Advanced Topics in Software Analysis and TestingLectures are relevant to assignments
Assignments are interesting (mostly C++, one Typescript, one Java)
There is an exam for the lessons not covered in assignments
5% extra credit from participation
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
vyP8VyExLVMcHYPxZVnhQg==2025-08-20T23:05:50Zsummer 2025
High Performance ComputingThis was my second course at OMSCS, and this was much harder than I could have anticipated. Firstly, taking this course in the condensed Summer semester is not recommended. This course is already extremely dense, and the assignments require a lot of time investment. Definitely take this course alone. This course is very theory-heavy, but for good reasons.
That being said, this was a very rewarding course, where discussions with other students greatly increased my understanding of the material. Despite the heavily theoretical material, the lectures are very well structured, and the TA's are very supportive.
There are 2 exams in this course, and they are both very difficult, however, both exams are curved. The course also had adjusted grade scales, where a C was >=64%, B >= 74.5%, and A >= 85%. Start the assignments as soon as they are available, as Spring and Fall semesters have 3 weeks between assignments, whereas Summer semester only has 2 weeks between assignments. There is also an extra credit assignment towards the end of the course, which in our case was worth an additional 10% of our grade.
Rating: 5 / 5Difficulty: 5 / 5Workload: 18 hours / week
vyP8VyExLVMcHYPxZVnhQg==2025-08-20T22:54:48Zspring 2025
Graduate Introduction to Operating SystemsI took this course as my first in OMSCS. As someone who has previously taken an OS course in undergrad, this course was a lot more engaging than the other, where the lectures were well structured, with a toy store analogy running through the entirety of the course, which helped contextualize these concepts very well.
That being said, this course is definitely on the harder side. It is jammed packed full of material, with very rewarding assignments. However, the exams are very difficult, with them constituting half of your grade.
The time investment on this course was on the higher end, as some of the assignments can have you scratching your head for a good number of hours, with the difficulty only increasing as the assignments continue, definitely set aside a lot of time for this, and take it alone or with a lighter workload course alongside.
Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week
sdvfEoGvnS6Gh4tetk1XCw==2025-08-20T03:20:34Zsummer 2025
Artificial Intelligence Techniques for RoboticsI took this course over the summer so I believe the pace of assignments is a little bit faster than in a normal semester. I have a background in CS, this was my 5th course in OMSCS and I ended up with an A. Overall, this course is very manageable and the autograder is fairly lenient for the projects, which is nice because it can get pretty frustrating tuning your solutions to pass all of the test cases. I felt that the projects in this course were satisfying and I do feel like my Python skills improved in the end. I feel like I gained a good introduction to some robotics concepts and was genuinely interested in the course content throughout. Additionally, the TAs were really active on the Ed forum. My complaints about the course are that the lectures have a lot of typos, which made them a little bit frustrating to watch at times, and as I already mentioned, the projects can be time consuming when you have a working solution and it boils down to tuning. I personally ended up just calling it good enough for a few of the projects and settling with a mid-range A instead of spending many more hours for the 100. For a few of the concepts, I also had to spend some extra time with additional resources they provided in the course and some external resources as well to really understand them. I found the exams to be relatively challenging but fair. You get two chances to take them, but the questions are different. And notably, I and many others found the second time to be more challenging. Whether that was random or not I don't know, but just keep in mind the second time could be harder.
My advice for people taking this course would be:
Overall, this course is worth taking in my opinion. I would consider this an easy A class if you just put in some effort. It was a fair pace for summer and I think if you have a lot of time to spend on school, it could definitely be paired with another course in Fall / Spring.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
X/XKjRvrkYmriHluqQ/7Bw==2025-08-19T03:14:08Zsummer 2025
Machine Learning for TradingDon't believe the average stats from reviews, this class was made significantly more difficult and time-consuming since it was handed over from Prof Balch to Prof Joyner. Now it's way too heavy on reading and writing and was my least favorite class out of the 6 that I had already taken in this program. There was just way too much effort doing pointless tasks that don't result in any learning because a lot of effort was put into paying attention to incredibly minor details in both the projects and exams.
Project descriptions are way too long with information and massive deductions scattered everywhere. You can literally get 20% off a project that's worth 25% of your grade for putting the wrong line color on a graph with a single line. There were some complaints about pointless details and the justification was that they needed to include these fine details to make sure that you were reading the instructions and weren't cheating.
At the same time, the rubric can be too specific and too vague at the same time, also resulting in massive deductions. There are some places where the grading criteria is "sufficient detail" which just opens up a can of worms on how much detail is sufficient. Compared to when the class was taught by Prof Balch, there's just way too much emphasis on the writing aspect that makes the time spent on projects to easily be 20 or even 30+ hours each project (with a report). The final project even took me over 40 hours due to writing and building up on project 6 which was ridiculous since there wasn't much learning but rather rote tuning your model and writing about it.
The exams are also nearly insufferable due to being composed of multi-checkbox questions which turn it into a 100+ true/false question exam. True/false questions are horrible because everything becomes a trick question and the inclusion of a single word or comma becomes more important than actually understanding concepts. Plus some of the exam questions are on stupid, irrelevant details that have absolutely nothing to do with machine learning or trading. Without giving any specific details to respect the honor code, the questions are about as relevant as asking what was the weather in a particular city on a particular date.
Oh, and don't bother watching the lectures as the additions to the course have made them nearly irrelevant to the class despite being so enthusiastically produced by Prof Balch. The exams were disproportionately focused on the required readings which were so abstract and far beyond the scope of the lectures that I often wondered what the readings even had to do with the course other than to provide a question bank to ask very specific details off of.
I had an A for 3/4 of this class but lost massive points on a project worth >20% of my grade due to said issues with line color and "sufficient detail" which brought me down to a low B and made the rest of the class incredibly stressful. I squeaked by with barely 2 points over the threshold for an A but spent >25 hours each week. The changes have made this not an easy class anymore and it was by far my least favorite class in the 7 that I have taken in this program. There were "harder" classes like GIOS and AOS that took me less time just because they were way more interesting and didn't waste your time on tiny pointless details and going on a model-tuning fishing expedition.
Rating: 1 / 5Difficulty: 5 / 5Workload: 28 hours / week
sMGfjdC0D9guZn30kGqjbg==2025-08-18T21:52:41Zsummer 2025
Human-Computer InteractionThis course was very helpful to ease into the program. Joyner does a great job teaching the subject while keeping it interesting. I had no problems getting high marks on the papers by ensuring I followed the rubric. The tests were straightforward and open note, so they ended up being fairly doable. The only part that causes me grief were the quizzes. These are close note, short answer questions that took me the whole time to answer. I did great on the first two quizzes and OK on the last two. Overall ended with an A and the course was great to get me back into the swing of school. I recommend taking it seriously, but this course makes for a great summer class.
Rating: 5 / 5Difficulty: 3 / 5Workload: 12 hours / week
a9PlvSp4B/hPzSB/H2oE5A==2025-08-18T19:16:25Zsummer 2025
Data Analytics and SecurityThis is my fifth OMSCS course. I chose it as an easy summer class, and it is easy—the curve means almost 90% people gets an A?—but I wouldn’t recommend it. The material is shallow and the coding assignments feel random; I’d rather solve a clear problem than explore without a goal. As others have noted, it’s basically Python/R 101. The grading rubric is vague: I took an unexpectedly low score on the first part of the final assignment, which dropped my grade from 99 to the mid-80s. I eventually earned an A by writing the final paper in a way more academic style—something never made explicit—but the whole thing felt like guesswork compared with my other courses. Even as a light companion to a tougher class, I’d skip it for your sanity.
Rating: 2 / 5Difficulty: 1 / 5Workload: 3 hours / week
5gzgma/eRK6s/VzvgWN7Ww==2025-08-18T18:49:37Zsummer 2025
Deep LearningI think this is an interesting class with quite well organized lecture, reading materials and assignment. I spent on average less time on the assignments (~20 hours) than the one in ML classes (~35-40 hours). There are 5 close book quizzes in the summer, so it kind of force the student learn the principles and algorithm, which is beneficial. Also enjoy doing the group project, make sure to find reliable teammates!
Rating: 5 / 5Difficulty: 4 / 5Workload: 20 hours / week
XzosBcdhnDW15JHR+dckJw==2025-08-18T04:28:18Zsummer 2025
Special Topics: Business Fundamentals for AnalyticsThis course combined both the best and worst components of OMSA. Despite not looking forward to yet another required watered-down business class, I thoroughly enjoyed the first 75 percent of the course. Unfortunately, the Marketing module was so ineffective and disengaging that it tainted my overall experience. Final grade: 97%.
The Good
The Bad
TLDR Excellent TAs, self-assessments, and simulations, but the Marketing module was weak, exams relied on memorization, and the course lacked rigor. An easy A that shows both the best and worst of OMSA.
Rating: 3 / 5Difficulty: 1 / 5Workload: 12 hours / week
WtbvySDnlL3VacxhINR7DQ==2025-08-17T21:20:59Zsummer 2025
Natural Language ProcessingAn excellent course. Lectures are very informative and well structured and I learned a lot from the projects.
Easily one of the best courses I've taken so far (7 courses in).
Rating: 5 / 5Difficulty: 5 / 5Workload: 6 hours / week
K6NJirXopbAX3IHW+z3fuw==2025-08-17T00:37:32Zspring 2025
Network SecurityI have based my ratings here for difficulty and workload on manual student work excluding AI tools (When class policy excludes them).
TLDR: Take this class if you know what you are getting into and are ready for battle. Curves are generous and an A is absolutely within reach if you're not new to the topic and/or are willing to grind it out.
This class seemed to be harder on me than most. I came in with an almost nonexistent security background and middling general tech exp. For whatever it's worth.
Most of your grade is project work done in modules on provided VMs with instruction sets that were fairly vague. For this cohort, our absolute All-Star of a Head TA (Melissa) made a huge difference. By combing transcripts of office hours, Ed, and the class Discord, I felt MUCH more confident in approaching known sticking points. Also, the grader is mostly transparent if I remember right, which is a huge plus.
Despite the difficulty of the projects themselves, the instructional team was VERY generous with curves and extra credit. So if you want a class specifically on this subject with an emphasis on simulated attack/defense scenarios, I would take it. If you want an easier A, I wouldn't.
Rating: 4 / 5Difficulty: 5 / 5Workload: 28 hours / week
K6NJirXopbAX3IHW+z3fuw==2025-08-17T00:03:44Zspring 2025
Machine Learning for TradingI have based my ratings here for difficulty and workload on manual student work excluding AI tools (When class policy excludes them).
TLDR: Until AI tools are allowed substantially or workload is reduced, do not take this class.
I think this is a class that has so much it wants to share with its students. It really does want its students to do well and learn a lot. Professor Joyner has proven this intent in my opinion. You can also tell by watching the lectures (Which you're also better off skipping to save time, sadly), that Professor Balch (The course's originator) was a true leader who also wanted his students to succeed. What I heard/read (Total conjecture here), is that the class was somewhat different back then, with more focus on coding and less focus on academic writing, of which Professor Joyner demands a LOT, in addition to what was already a complete course.
So, my belief is what you see in the combined ratings here reflects good will towards Prof Joyner, but coasts on expectations based on what the class used to be. It also reflects my belief that the student body (Who is relying heavily on AI tools to complete everything the course demands of them) is engaged in a bit of a course design battle with an instructional team who is possibly trying to work against AI tools by increasing raw workload rather than with it.
There are at least 30-50 pages (plus slides and lecture vids) to ingest each week, 12 short Quizzes based on them, 2 tough exams, 8 coding/implementation projects with mostly transparent graders, 5-6 7-10pg academic journal articles to write based on project work, and 2 pts of extra credit. With respect, no one is doing all of this with 8 hrs a week while maintaining academic integrity.
The exams and journal articles you're expected to write are where you will lose most of your final grade points. It is likely the most pedantic class I will ever take in my life, with instruction sets longer than the 7-10pg articles you will ultimately write. Buried in these elaborate specs are many, many minor-scope details, for which missing even one detail could knock you down a whole letter grade. When you consider that it is possible on a given assignment to lose enough points to total 100 many times over, I think you have to consider that something is wrong with the assignment's design, despite the instructional team's stated refusal to consider this to date. My advice is to use the spec for the project + research paper and follow/address literally every single sentence of the spec in your code and journal article.
The exams are worded strangely, likely in an attempt to curb the effectiveness of AI tools, which you are strangely allowed to use on the exams but not the assignments and articles. They are quite hard even then to prepare for, and are aggressively timed and curved heavily. I'd recommend a deep understanding of the underlying concepts of the readings using generated outlines as opposed to a deeper reading due to time constraints.
The TAs could be a bit more 'human', but all-in-all they were honestly excellent and professional. Per the norm, there is no contact with the Professor.
The tragedy of the course is that the material itself is not hard or particularly advanced. If it was pruned significantly, my personal belief is that students would better master and retain all key concepts the course conveys, and be less pressured to take shortcuts. But if I'm being honest, I would not take this or another class offered by Prof. Joyner again after this experience.
Rating: 2 / 5Difficulty: 5 / 5Workload: 30 hours / week
K6NJirXopbAX3IHW+z3fuw==2025-08-16T22:52:16Zsummer 2025
Network Science: Methods and ApplicationsAs always, I have based my ratings here for difficulty and workload on manual student work excluding AI tools (When class policy excludes them). TLDR: If you want an 'Easy' A at the expense of an emotional roller coaster, feeling as if your very grade is up to chance for most of the semester, this class is for you.
I'll try to be fair and not hyperbolic here. But in all honestly, this class is an embarrassment and it has really fractured my respect for the entire program.
Quizzes account for a significant part of the total grade and they are essentially open-book. I like this in theory, but the problem is that questions are extremely poorly worded, thought through, and tested. They are clearly written by someone without the level of English necessary for their proper formulation. Quiz questions at times are empirically impossible to solve, due to either poor grammar or to edge cases the instructional team has not considered. When legitimate calls for clarification are raised on ED, they are often shot down unprofessionally. The response tends to be, to paraphrase: "Deal with it. We'll fix it with a curve later. Stop crying about it." Or just silence.
Projects, which are more like walkthroughs with unit-level challenges via jupyter notebook, account for the bulk of your final grade. For the most part they're reasonable, both in terms of raw workload and difficulty. Where it fails is, as before, with poor curriculum design and administration. Entire sections of certain assignments were unsolvable due to under-prepared notebooks, helper functions, and/or broken opaque graders. The running joke among students was to wait as late as possible to complete each assignment, to minimize time and stress wasted due to such errors. In one particular assignment, many of us received failing grades. After the shock wore off, it became clear the onus was on us to manually report the problem individually. God only knows how many graded assignments have otherwise been accepted as final and impacted final letter grades or worse. We are criticized at the program level for 'grade chasing', but now I'm starting to believe this may be a necessary practice after this experience.
The head TA was mostly responsive and professional, although showed a lack of maturity in acknowledging the well deserved feedback from students, and also in accepting responsibility for the incompetence of his subordinates, whom in many cases left entire assigned discussion threads unattended at key times.
If I could go back in time, I do not think I would take this class without a new instructional team and new materials.
Rating: 1 / 5Difficulty: 4 / 5Workload: 15 hours / week
n09SLVGtZHm61UNoxOwaQw==2025-08-15T23:49:44Zspring 2025
Statistical Modeling and Regression AnalysisAs in every course in this program, and in life =), you get out what you put in, in both effort and intention. The right tools, resources, and support are provided, but should be paired with personal commitment and some tolerance. Canvas and other systems are not perfect, nor are students or TAs/professors. I did not encounter anything the instructional team failed to address properly and I always felt supported and guided. I found this class very useful, as it thoroughly covers the principles behind regression, deeper than in introductory courses. The slides are in R, however as part of a revamp, there is now sample code supporting the lecture problems in both R and Python. Homework and exams can be completed in either language. The professor and TAs are very knowledgeable and helpful. Office hours are especially valuable, as some TAs share a great deal from their own experience. Overall, the instructional team consistently tries to add value and contribute. I had no issues with the video lectures, but for those who prefer it, the transcripts paired with the corresponding slides are available in a single document, not all courses have that. Exams are now open book, whereas before they were not. In my opinion, HWs and exams do evaluate the understanding of concepts. As with anything new, some memorization is needed, but it’s up to you whether to stay at that level or really make sense of the concepts. I definitely got a lot out of this course. Chose to complete it in Python, fingers crossed that Time Series will be available in Python too.
Rating: 5 / 5Difficulty: 3 / 5Workload: 15 hours / week
UVvO6DCuCk74Qptze6p9WQ==2025-08-15T20:45:48Zspring 2025
Machine LearningThis course is very difficult, but I also think some may be too harsh on it. I've been told that grading comes down to the TA that you have, so I'll simply discuss my experience with the course and you can take it or leave it:
The Pros:
-You will learn how to use Python libraries and you will learn the fundamentals of machine learning. -While ML4T projects were focused on you understanding how to program the various ML algorithms, all algorithms that you need for the projects exist in pre-established libraries - you can even use AI to help you code. -The focus is on the application of ML algorithms, particularly in terms of analyzing data, how to use the various techniques, train learners and avoid overfitting. -Lectures are fun an engaging for newcomers -If you like writing, and find yourself to be good at it, you will enjoy this course -Class is curved!
The Cons:
-If you don't like writing, this may not be an enjoyable course for you -While the material is good for learning the fundamentals, it is a touch outdated -While the lectures are entertaining, they are extremely verbose - I recommend watching them at a faster speed.
With all of this in mind, I highly recommend this course for anyone who wants to go down the machine learning path, without a ton of knowledge of the field, and here are my tips that I hope will help you succeed and hopefully ace the course:
For whatever my results are worth, my grades on the projects were 86, 91, 103 and 85 respectively as well as an 80 on the final, and my grade was curved up to an A. Best of luck all.
Rating: 4 / 5Difficulty: 5 / 5Workload: 25 hours / week
CprK70rjk/yNl8zylh2baQ==2025-08-14T19:12:04Zsummer 2025
Artificial Intelligence Techniques for RoboticsAI4R (now Robotic AI Techniques; RAIT) was a great survey course discussing some major approaches to robotic localization (where are you?) and control (what should you do?). There's enough math that a light stats and/or linear algebra refresher could be useful, as it will help you develop a better intuition for the concepts.
The midterm and exam are of middling difficulty, Honorlock proctored, and quite fair overall. The meat of the course is in the project and problem set work, where the problem sets will introduce practical code-based approaches to the concepts presented in lecture, and the projects provide a more substantial exposure and challenge involving the key concepts. The code framework is robust, with test cases and visualizers available to inspect your code's behavior. The overall course load, even for summer, is quite fair, though if you fall behind you're going to have a bad time. I personally advise trying to stay 1-1.5 weeks ahead, i.e. finish things a bit over a week before they're due, to ensure you have time to spend on the later, more difficult projects in the course.
It's a bit of a lighter course, but very interesting and fun. The TAs are extremely organized and helpful, and I would strongly recommend this course if you're considering it.
Rating: 5 / 5Difficulty: 2 / 5Workload: 10 hours / week
FNE7KG9UHITnWcBoMYKKlQ==2025-08-14T11:33:00Zsummer 2025
Machine LearningThe syllabus of this course has recently changed, adding three unit quizzes and two post-grade remediation opportunities (summer workload). Overall, this should be a good thing. Specifically, the quizzes make the final grade more manageable—especially with multiple attempts available, you can consistently secure around 90% on them. Although I didn't use the assignment response remediation, it should be helpful. My average score across the three assignments was also around 90%, so even without thoroughly preparing for the final exam, I still received an A. The final exam was quite tough and overwhelming—it seemed that only those who truly understood all the material could perform well on it. Since I have a background and some experience in machine learning, most of the course (except for the game theory part) was relatively easy for me. Taking everything into account, I would still rate the difficulty as 4 out of 5. Also, the workload I input is for average 3 weeks, that means, I spend about 2 weeks doing nothing, and 30 hours one week before due.
Rating: 4 / 5Difficulty: 4 / 5Workload: 10 hours / week
6wfsiy4f/touldY/xmD8Bw==2025-08-13T18:32:27Zsummer 2025
Machine Learning for TradingBased on the ratings, I thought this class would be super easy, but it wasn't as easy as I thought it would be. Definitely more time consuming than expected. Still a fairly decent and well organized class though.
Rating: 3 / 5Difficulty: 3 / 5Workload: 12 hours / week
4+N+5HpDIIcPjegkhLzJsg==2025-08-13T12:46:42Zsummer 2025
Introduction to Information SecurityIIS was my second course in the program (after ML4T), and I took it as an exploration of computing systems. I had heard it was a good first/early course in the program.
In one way, I strongly disagree that it's a good first course in the program. Your grade is entirely based on your project grades, which are a collection of capture the flag challenges that require you to learn the basics of various types of exploitation at various levels of modern computing systems. This means that your grade is not at all based on whether you do any reading or watch any lectures or can pass any exams. So in the sense that a first course in the program is intended to acclimate you to the kinds of work you are going to need to do as an online student throughout the graduate program, this one absolutely does not do that.
But I think the reason that the course is sometimes recommended as a first course in the program is that it is a wide survey of topics that you can dive much deeper on, and I think that is actually a great reason to take it as a first course or an early course in the program. One week you're diving into javascript XSS attacks, and the next you're looking at buffer overflow exploits in C. You have to learn a lot on the fly, the challenges are really clever and fun, and you get exposure to lots of technologies and ideas of modern computing through the projects.
I don't recommend this as a summer class if you've got plans for your summer. The turns are very tight for the projects (1 week each), and in particular, the binary exploitation project gave a lot of people headaches that would be worth at least two weeks in a spring/fall semester. I spent ~30 hours on it and went in with some C experience. But most of the projects are doable in more like 10-15 hours.
Ed discussions for this course can bring out the best and worst in classmates and TAs. Some of the challenges are hard to hint without giving too much away, so sometimes folks are giving really useful and thoughtful insights in their responses about how to think about a flag. But TAs have to redact a lot, which makes some folks who are lost fairly snippy and unprofessional. And collaboration outside of Ed/Slack is strictly prohibited, which can be jarring for folks used to forming study groups or Discord servers to figure things out.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
Hlbv1xErB9n1pHPQKEPY4Q==2025-08-12T20:01:48Zsummer 2025
Computer NetworksCS6250 - CN - Is not a serious master's level course. The 4 coding assignments are trivial, and the quizzes' and exams' MC questions are taken nearly word-for-word from the lectures. Quizzes are open-book while exams have you install a spyware on your pc to ensure you are not opening other tabs (recommend just booting from a live USB).
Since they are all MC, obviously memorization of the theory is all the professor is teaching you. However, the level of rigour here is more like that of a high school class; it is all only a high level overview of the topic. "Lectures" are all just short webpages you read (no textbook) with a few of them having optional Kaltura narrations by the professor. There are "required" and "optional" research papers but you won't be tested on either - so why bother reading them? I honestly believe I could've learned all the content in a 2 hour YouTube video.
Rating: 2 / 5Difficulty: 2 / 5Workload: 5 hours / week
3umnwGrvcYw4jxaYF/rTnw==2025-08-12T18:13:15Zsummer 2025
Network SecurityOverall solid course. Quizzes are open book and projects are straightforward. Only issue is sometimes the VMs are buggy or have device specific configuration issues that can be challenging to debug.
Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week
UPMquKZjOzk3B7vxhaaTkA==2025-08-12T13:03:33Zsummer 2025
Introduction to Information SecurityI should probably preface by saying I had to end up dropping the course because it was getting very difficult to manage coursework and work along with married life. I was really excited to take this course since before applying to OMSCS in Summer 2024; given the seemingly lighter reviews for the course I thought it would be a good summer course. Turns out right as I was applying for the program Dr. Lee decided to add a 9th project for the course and made them all for credit compared to 6 projects for credit in previous summers and 8 projects for full semesters.
For the 9 projects: Each project was assigned out on Thursday night and would be due on the Sunday night the week after. So 9 days for each project with some overlap on new projects being released with the current project still not due yet. I already had a slow start to the course with the first project since capture the flag style projects were very new for me and then taking longer to finish on that projects and then subsequent projects created a vicious cycle for the rest of the course. After getting a 30% on the binary exploitation project and spending probably 50 hours on it I realized I was f**ked. Ironically that project had the most helpful and gracious TA of the course. Speaking of TAs...
TAs: Look I really don't know how TAs in OMSCS are going through and I have largely given TAs the benefit of the doubt and I appreciate all of their help for what I'm assuming is a crap pay but holy hell can the ones in IIS stop with the massive God complexes? They seemed far more concerned with catching cheaters than actually helping students and when the topic of virtual machines was brought up their response was that they won't try at all to accommodate for Mac users.
Overall I still think this is a cool course that's different than others. I know there were plenty of others in similar situations but it was hard to balance schoolwork and work. It seems like a lot of students got A's in the course so most important lesson I learned is that summer courses just aren't for me. I'll retake the class in a future full semester. I also recommend this course later in your omscs career. I really had no life until I dropped the course. Also it's pretty demoralizing when you're hard core struggling and everyone in Ed is posting about how great the class is going for them, but what can you do.
Rating: 3 / 5Difficulty: 5 / 5Workload: 20 hours / week
78Rx++Fcja9Dy/E2nOh1ng==2025-08-12T02:59:24Zsummer 2025
Machine Learning for TradingTLDR: Grade: A Hours: 15-20 per week in summer term Tips: Put in the effort and do well on the projects, as the exams can be tricky no matter how much you study
If you are at all comfortable with undergraduate-level finance, statistics, probability, and/or ML, I would avoid this class. The topics covered are introductory and I can honestly say I didn't learn much. However, I am biased as I took many finance, statistics, probability, and CS classes in undergrad and have also worked with these topics professionally for years.
At the risk of sounding whiny, there are also way too many source materials to keep track of. On any given week, you are expected to read Chapter 2 from Book A, Chapter 8 from Book B, and watch Videos 2 through 8, etc. The constant context switching is a killer. Oh, and the video and audio are sometimes potato-quality.
I got high 90s-100s on all the projects and 80s-90s on the exams. Just review your code and reports and ensure that you've hit all the project specs and you'll be fine.
Overall, I could see this class being a good first or second class for someone who doesn't have much prior experience with finance or CS/ML. But for me it just felt like busy work.
Rating: 2 / 5Difficulty: 2 / 5Workload: 15 hours / week
r85SdD7EI19H4+C884j5Og==2025-08-12T00:08:33Zsummer 2025
Introduction to Information SecurityPretty chill class overall. Material isn't the most interesting and the CTF project structure takes some getting used to. Most of the projects were very manageable with the weekly deadlines. Machine Learning and Binary Exploitation took much more time than the other projects. Biggest thing I learned is I much prefer projects that build on concepts over time rather than a bunch of cursory introductions to topics.
Felt like I didn't really learn all that much by the end of the class. If I had to do it over again I'd probably skip this course, there are many other courses that have more interesting/valuable content.
Rating: 2 / 5Difficulty: 3 / 5Workload: 10 hours / week
QJfnPoFqMua2oeZuSZNI1g==2025-08-11T21:49:07Zspring 2025
Artificial IntelligenceGreat course. If you're looking to get enough pre-req info for all other AI/ML courses at GT, this is where to start. It goes moderately in-depth on everything AI/ML but not too in-depth. The projects are genuinely interested, challenging, and enjoyable. Lots of flexibility with the exams and course timeline. Strongly recommend taking this as the first course in the program.
Rating: 5 / 5Difficulty: 4 / 5Workload: 12 hours / week
QJfnPoFqMua2oeZuSZNI1g==2025-08-11T21:44:04Zsummer 2025
Machine LearningFull time SWE with top 5 CS undergrad with lots of experience taking graduate-level courses.
This course was brutal, at least during the summer semester. However, I learned an incredibly large amount of content during the course. The reports, although time consuming, are incredibly informative at helping you understand the content and also improves your paper-writing skills. The quizzes are fair and allow multiple attempts. The paper resubmission is also fair if you can squeeze in the time to do it. The lectures are good, but not perfect. Some sections are hard to follow no matter how much textbook you read.
Advice for future students: 1.) Be cautious of taking this in the summer. Prepare yourself for a stressful summer. 2.) Read teapowered ML Notes. 3.) For the reports make sure to have an explanation for every single result you achieved. Base your explanations on course content and ML theory. Anything that is "suggested" or "recommended" in the report handout is secretly a "REQUIRED OR YOU WILL LOSE POINTS". Don't. Skip. Anything.
TLDR: Extremely stressful in Summer Semester. Super informative though.
Rating: 5 / 5Difficulty: 5 / 5Workload: 20 hours / week
GH13bbOvqcOgp7Ysaqty0A==2025-08-11T19:08:21Zsummer 2025
Artificial IntelligenceGenerally, this is a challenging but doable course. I ended up with a 98.5. Workload is NOT consistent throughout the semester. PLEASE NOTE, for the summer term, you will not get less material covered nor fewer assignments compared to the regular semester.
Each week, you will have some chapters to read, lecture videos to watch, one or two ongoing assignment, and a challenging quiz (for extra credit). I spent more than 30 hr/wk during week 2 - 4 and much less in the second half.
The material for the first half of the semester is way much better than in the second half, with particularly poor-quality material in the deep learning and planning under uncertainty sections. Read the book can really helps
The TAs are fine, most TAs hold office hours and respond to questions on Ed threads. However, there were frequent corrections and clarifications needed for both exams.
Course components:
Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week
vt/awCkTL2KApu8V21euXw==2025-08-11T12:37:07Zsummer 2025
Machine Learning for TradingThis course serves as than introduction to both finance/trading and machine learning. I took this as my second course in the program (GIOS in Spring '25). Overall I thought it an interesting course, which was a bit more time consuming given the condensed summer schedule. The finance side of this course I thought was relatively light, so if you already track financial markets in the news I would way you probably will be pretty easily able to pick up what's covered there (or you may already know details and it may just be review). On the ML side the concepts of supervised learning, unsupervised learning, and reinforcement learning are introduced. In my opinion this is where the value of the course lies, in providing some context to broad categories of learners and how they function. You go on to apply these ML concepts in a series of 8 projects. If you take this course I would recommend working ahead on projects 4 and 5 so you can spend extra time on project 6, 7, & 8 which are worth more of the overall grade.
The things I didn't love about this course were:
Rating: 3 / 5Difficulty: 3 / 5Workload: 15 hours / week
nTHM/jzaNwA0CjQ4/XQvOw==2025-08-11T02:57:11Zsummer 2025
Special Topics: Introduction to Computer LawThe lecture material was quite interesting, but to be honest, I found the project work quite displeasurable. I doubt most students will feel this way - to a normal student, maybe the non-coding assignments are a nice break from other classes. But, I think it's worth putting the word out for others like myself: if you find programming assignments fairly easy in general but struggle to stay motivated when the assignment isn't technical, you may have a tougher experience than others have indicated.
It's not like I would even disagree with the sentiment that this is an easy course. When there's no project to be working on, the class is <4 hrs/wk of commitment, no sweat. The problem is, the projects are fairly long reports that can get quite involved if you're trying to get a good grade. One of mine was >20 pages, and I felt quite miserable working on it by the end. We were not given any clear indication of what level of effort would be considered acceptable for that assignment, so part of that was me erring on the side of caution.
Granted, also, that there were only two such papers in the course, so the average workload was still fairly low. But those papers easily took 16-24 hours each, and it was a complete slog for me. I did not have any fun at all working on those papers, although I really enjoyed watching the lectures. So, if you're on the fence about taking this course because you're used to programming assignments and this is "different," consider my experience a data point in favor of taking something else - in my case, this was me trying something a little different, and it was not nearly as fun for me as I had hoped. But your mileage may vary, of course.
Rating: 3 / 5Difficulty: 2 / 5Workload: 10 hours / week
LJKfaWW6btBGa/b7qEB9UQ==2025-08-11T01:50:53Zsummer 2025
Digital MarketingOverall it is an easy A course for sure and a good fit for summer semester:
It is not a CS or CSE course so you should not expect much coding experience around. However, it is a good summer course for anyone wanna take a mental break.
Rating: 3 / 5Difficulty: 1 / 5Workload: 4 hours / week
KFrNxTRmsMecLDSsPl0/0Q==2025-08-10T22:35:14Zsummer 2025
Human-Computer InteractionOverview This was my first completed class after dropping my first semester last year, and it’s a perfect beginner course for learning how OMSCS works or easing back into education.
Course Structure The summer session is very front-loaded with heavy readings, videos, and assignments early on. Prof. Joyner’s videos are excellent, though the readings can feel scattered and cover many topics without much depth.
Assignments Homework is straightforward—follow the rubric, stick to JDF format, and use Overleaf (LaTeX). Start the individual project early, as the team project is essentially the same.
Team Project Tip Pick your team members early if possible. My proactive group started early and finished most of the project well ahead of time, which made the process much smoother.
Exams and Quizzes Tests are open-note—merge PDFs for quick searching. Quizzes are heavily weighted, so avoid doing poorly on more than two areas to protect your grade.
Final Thoughts Overall, it’s an approachable class if you stay disciplined through the intense start.
Used ChatGPT to enhance my message for clarity and conciseness.
Rating: 4 / 5Difficulty: 2 / 5Workload: 10 hours / week
78Rx++Fcja9Dy/E2nOh1ng==2025-08-10T20:19:48Zspring 2025
Database Systems Concepts and DesignThis was my first OMSCS class and I found previous reviews about it to be pretty accurate. I spent 15-20 hours per week on this class and got an A. However, take this with a grain of salt as I work as a full stack SWE and was already comfortable with databases, backend, and frontend before starting the class.
To echo other reviews, I strongly recommend learning backend and frontend development before taking this class, and/or finding teammates who have this skillset. This will save you a lot of time and headache for the group project, particularly Phase 3. Luckily for my group, I'm comfortable working across the stack and was able to unblock the team on database, backend, and frontend issues, so our development went smoothly and we did very well on the project.
This is a class about databases, so I'm still confused why students are expected to know frontend development and build a fully functional full stack web app. There is zero lecture or reading material about API or frontend development, so it doesn't make sense why so much of the project grade hinges on having a functional app and UI.
Regarding the book and lectures, I actually did find much of the material to be interesting and useful. Of course, you won't use relational algebra and calculus directly in a SWE job, but it's important to develop analytical thinking and understand the concepts behind database design.
Lastly, the exams. I didn't feel that they were intentionally trying to trick students. It's just that missing or misinterpreting a single word can change the meaning of the entire question/answer. So be sure to carefully read and re-read all questions and answers before submitting. Exams 1-3 were fair. Exam 4 seemed to be intentionally difficult, possibly to bring the class average down? I scored in the 90s on exams 1-3 and all project phases, and a 60 or 70-something on exam 4.
TLDR: Grade: A Hours: 15-20 per week Difficulty: 3/5 Overall: 4/5 Tips: Learn full stack development and carefully read all exam questions
Rating: 4 / 5Difficulty: 3 / 5Workload: 15 hours / week
wLMhvXbZKQ3IINBkNGi+iw==2025-08-10T16:54:11Zsummer 2025
Machine LearningI'll start with the good:
The topic is interesting. The course is divided into three units: supervised learning, unsupervised learning, and reinforcement learning.
The lectures are OK - they are at a high level and not very rigorous but mostly easy to understand.
I wasn't a big fan of the textbook (Mitchell) - it felt too verbose, spent a lot of time focusing on areas that are not relevant to modern ML, and didn't have a lot of opportunity for practice.
The bad:
The instructor: while nice, he was very disorganized, changed the syllabus multiple times, took a long time to grade the assignments, picked out terrible datasets, and set up this bizarre system where the rubric for the assignments is not given out to the students.
The TAs: they let students respond to most posts on Ed, and there was this one guy (student) who responded to nearly every post with half true information that he copy pasted from Chat GPT. When they do respond to questions they give a vague response and do it several days later. During office hours, the head TA acts like he is God's gift to ML, and will be dismissive if you question his answers. There were errors in their keys to the practice tests.
The assignments: as above, bizarre that the instructor wouldn't release the rubric to the students for the assignments. How are you supposed to know what you are being graded on? Eventually you can kind of guess that they are looking for you to vomit out key words from the lectures and readings, even though the explanations are incorrect / imprecise. The "model" assignments that they released after grading were riddled with errors and omissions but at a superficial level sounded good. The grading is extremely subjective and basically depends on which TA you get assigned. They will comment that you left out XYZ, even if you didn't, and if you want to get points back you have to submit a two page written response as to why you actually did include XYZ. One of the datasets is synthetic and entirely implausible -- it was supposed to be a dataset that represented the incidence of cancer in different populations. As an example, there were equal numbers of men and women with prostate cancer. We were told to basically ignore any understanding of the data and just crunch the numbers and report the results, even if the analysis didn't make sense. What's the purpose of that? It is, again, bizarre, that the students are being told to ignore outside understanding of the problem they are trying to solve to just get the "right" answer for the course. The other dataset is another low quality dataset from some Taiwan Banking journal that appears artificially manipulated with numerous mislabels and errors, and we are supposed to use this dataset to perform our analysis. They are looking for you to try and BS your way into a cogent analysis of a garbage dataset. The un-supservised learning assignment was a particular waste of time because both of these datasets had trivial, noisy clustering, but the instructors were looking for students to try and interpret the noise to stitch together a plausible-sounding analysis. You can try and get a better idea of the assignment if you scour through 100+ ed posts and listen to the office hours for hidden clues as to how to pass the assignment. Most of the time you spend on the assignment will be guessing requirements, guessing what type of analysis they are looking for, interpreting noise, formatting figures, and padding your word count so that the report seems more legit.
Quizzes / Exams: they were riddled with errors and vague questions. they very loosely corresponded to lectures and some of the quiz questions were on topics listed as "optional" on the syllabus.
The class had to be heavily weighted because the instructor can't figure out how to make a course that is challenging but fair, so instead he throws people into a stressful stochastic simulation of a class that results in a large standard deviation for course performance and then adds a generous curve so it doesn't look like everyone is failing the course.
You may need to take this course for your specialization, but otherwise I would suggest self study. They need a new instructor to revamp the course and make it more worthy of OMSCS.
Rating: 1 / 5Difficulty: 3 / 5Workload: 16 hours / week
WY9ryZpTlXAEbBawHnpegA==2025-08-10T13:51:39Zsummer 2025
Artificial IntelligenceThis review comes from the perspective of someone with a full time job and a wife and child. In essence, school comes third for me, even though it's still extremely important. Additionally, after getting my first degree in Software Engineering, I joined the military for 4 years - so my software engineering skills had 4 years to atrophy. All this to say: I may have less time to devote and fewer skills developed towards this class. The average GA Tech student is likely more well-equipped than I am.
The material itself is really cool. The assignments (weekly or bi-weekly projects, basically) walk you through the things you need to do at a high level. The implementation can be tough, but it's not unreasonable. I was really impressed by most of the projects as far as "I never thought I could do that on my own." I did well enough on the first 5 assignments that I could skip the last one. This gave me breathing room to study for the final.
Besides assignments, most of the rest of your grade will come from the midterm and final. You may notice that the course is curved. Don't do like me and assume that, just because the course is difficult, that the curve will be substantial. In reality, it only shifts the grading buckets by at most a few points. I didn't put nearly enough effort into the midterm and got a correspondingly low grade. Luckily, I was able to do well enough throughout the rest of the course to recover with a B.
Some people complain about the plagiarism warnings and threats of OSI violations. It's not that bad, you just can't use google and ChatGPT. Which is fair. When you use approved libraries, you are authorized to read the documentation, which is more than enough to figure things out (remember, I'm probably less skilled than most of y'all). The inability to look up examples outside of what the book or TAs provide makes things more difficult, but really that's a good thing. Instead of copy/pasting someone's code, you'll write your own and learn it.
The course is tough, but it's not unreasonable. They provide enough resources to figure things out. My biggest advice would be to start things early enough to look through their resources and learn them. It's a huge disadvantage to wait until the weekend before the project is due in order to start the assignments.
Before the midterm, we had about 1 assignment every 2 weeks. After the midterm, assignments overlapped and I ended up doing 1 assignment per week for the rest of the semester. That was rough, as I got myself into the aforementioned and warned situation of working the project the weekend it was due. Despite work and life commitments, though, I was able to figure it out.
The lectures provide high-level intuition, and you'll have to go into the textbook to learn details and specifics. I could not have finished this course without the textbook. Having said that, the lectures will inform you on where to go to look up information in the book - that is, you don't have to read hundreds of pages. You just go to where the information is and read a few paragraphs, maybe a couple pages at most for a single topic.
The course is tough, but it's doable. If you have a lot of personal-life stuff going on, perhaps save this course for another semester.
Oh, for real review Linear Algebra. If the last time you took it was 7 years ago, like me. It'll pay dividends in the later assignments.
Rating: 5 / 5Difficulty: 4 / 5Workload: 24 hours / week
4v+GPNibbZHV4cDoKVlvvg==2025-08-10T02:50:01Zsummer 2025
Computer NetworksLectures are definitely on the dull side as others have mentioned, but not necessarily poorly written. They still taught me a lot, and I felt good about the amount I learned in the course.
Each lesson's lectures took ~3 hours, and projects took anywhere between 5-8 hours. Some weeks there were multiple lectures assigned, but this might have been because I took it in the summer.
Regarding exams: They could be a little better written, but you don't need to go crazy and memorize everything in and out like some people would have you believe. I answered the questions in the study guides provided by instructors, and got 90%'s on both exams. Exams only count for 30% of your overall grade, and getting near 100% on the other 70% (projects, open-book quizzes) is straight-up easy if you put in the time needed. That's not to say you won't struggle on the projects - I was banging my head against the wall for the first project for a couple hours, but then I read some forum posts, thought about it, figured it out, and got a 100%. So outside of the exams, you shouldn't miss many points.
On top of all of that, you can get another 3% back from extra credit assignments. That means you only need to get 17% of your overall grade to come from the exams; 17/30 = you need a 56.6% average score on the exams overall. Anyone should be able to do this, even if you're not great at memorizing.
Point being, reviews that note the exams' focus on memorization make a point that has some truth, but it is nowhere near to the point where it should affect your grade unless you really mess up in other areas of the class.
Rating: 4 / 5Difficulty: 2 / 5Workload: 8 hours / week
7o9Z/9juCh8XotdKJOWDiA==2025-08-09T15:24:54Zsummer 2025
Human-Computer InteractionI took CS 6750 HCI during my last semester, Summer 2025. I really liked the content from day 1 and it was so much of learning. The invidiual project was good and I got 100 out of 100. The team project was also good and got 98 out of 100. Overall, I got grade A in it. I will surely recommend this course to everyone
Rating: 5 / 5Difficulty: 3 / 5Workload: 8 hours / week
sO8OJlQ/P8sVDM5eftGHRA==2025-08-08T20:01:42Zsummer 2025
Introduction to Information SecurityI took this course because I work full-time and don't like to overload myself with something overly difficult during the summer semesters. I chose IIS because I’d heard it was “not terribly difficult,” and since I work in cybersecurity, I figured it wouldn’t be a problem.
Well, maybe that's true during a regular semesters. The summer session, however, turned out to be pretty challenging at times. Each lab varied in difficulty, ranging from trivial (PCAP lab – about 5 hours total) to demanding (Binary Exploitation and Web Security – 12 to 15 hours each). Binary Exploitation in particular seemed to be the most objectively difficult, as many people struggled with it. The course covers a lot of topics, so chances are you’ll hit something that stumps you for a while.
In the summer semester, you complete one lab per week, and the short turnarounds on the labs made the course more challenging than I expected. That said, I still enjoyed it because it gave me the opportunity to practice on my skill sets. For someone who has never taken a security-focused course, it offers a clear look at why secure development and coding practices matter, and how things can go oh so wrong (Log4Shell).
Overall, I’d give the course a 4/5. The machine learning component felt more tacked-on than security-focused. Also, there’s plenty of room to earn an A even if you bomb one assignment, you just have to work for it.
Rating: 4 / 5Difficulty: 3 / 5Workload: 12 hours / week
4rk4PJ3PGaGuqNEnhVBcMw==2025-08-08T17:59:11Zsummer 2025
Introduction to Graduate AlgorithmsThis class is tough, as others have mentioned. I believe it’s designed this way so that earning the degree truly feels like an achievement. Having all exams for grading makes it even more stressful especially since, for many of us, this is the final course before graduation.
Don’t lose hope if you score low on one exam as you can still recover. In my opinion, Exam 2 is the easiest, while Exam 1 is the toughest as getting that optimal solution is most. Think through every possible variation of the homework problems for exam preparation, and review all the materials and announcements posted by the course staff.
This is one of the most organized courses I’ve taken, and the Head TAs are extremely dedicated working days and nights to help us. I even got a reply at 1 a.m (Thanks Jamie). after Exam 2 grades were released. Also, thanks to all TAs who worked hard for getting HW and exam graded out in short summer period.
Rating: 5 / 5Difficulty: 5 / 5Workload: 23 hours / week
ZduXZVe+NcBIRDua2dy92A==2025-08-08T14:15:23Zsummer 2025
Computer NetworksTook this course over the Summer to catch my breath after AI (Fall 2024) and DL (Spring 2025) and before ML (Fall 2025) and IGA (planned for Spring 2026). For my purpose, the course almost worked.
The good:
The bad:
The final note: some reviewers compare this class to KBAI. I strongly disagree. There is no class (or at least I have not taken one) worse than KBAI. In CN, at least I learned something and it was not nearly as stressful and required nearly as much stupid work as KBAI. I really cannot understand why KBAI class even exists, it has no reason to.
Rating: 3 / 5Difficulty: 3 / 5Workload: 15 hours / week
AqdW0TmiDdSgsdG+xFWuYA==2025-08-08T04:54:15Zsummer 2025
SimulationI must say, this class took me by surprise. It was my 7th in the OMSA program and the first where I earned a B (all A’s until now).
Like other students, I found Dr. Goldman’s style and humor made the lessons genuinely enjoyable. A big plus.
That said, there’s room for improvement. First, I wish the Arena lessons were updated to reflect the current interface. Using recordings from years ago, when Arena looked very different, was both confusing and frustrating. Honestly, it might be worth replacing Arena entirely with a more relevant workplace tool (e.g., Python). The Arena material felt caught in between: too advanced for a simple intro/visualization, yet too basic to make us proficient. Even now, I couldn’t build more than the simplest models. I also disliked that the exams included questions requiring memorization of Arena-specific syntax, concepts or naming. In today’s world, this information is seconds away with a quick search, so testing recall over application felt outdated.
The exams were also more challenging than expected. We could bring a limited-size cheat sheet, but many questions relied on very specific formulas: if they weren’t on your sheet, you were out of luck. Again, it felt more like a test of memorization (or cramming as many formulas as possible in your cheat sheet) than understanding, which I find less relevant in a real-world context.
I certainly refreshed and strengthened my math, statistics, probability, algebra, and calculus skills (and would even say that I look forward to doing more in these fuilds!). However, as someone noted on Piazza near the end, I felt there was a lack of a holistic, “big picture” view tying it all together. The sheer volume of content (especially in the shorter summer term) may have been a factor, but I would have liked more emphasis on connecting concepts across topics. This was the first OMSA class where complex demonstrations weren’t just shown in the videos for you to follow along. Instead, you actually had to work them out yourself -pen and paper in hand- calculating antiderivatives to find probabilities under the curve.
Overall, Dr. Goldman is an exceptional teacher with great presence and delivery. But the course content could benefit from updates, a more modernized toolset, and stronger integration of concepts into a coherent whole.
Rating: 3 / 5Difficulty: 4 / 5Workload: 15 hours / week
C3Idv8ylYpFDQqlwsq904A==2025-08-07T21:47:44Zspring 2025
Graduate Introduction to Operating SystemsThe course lectures focus on OS design, but the programming assignments are more typical of a network + systems programming course using APIs rather than implementing an actual OS/Kernel. As other reviewers suggest, Beej's Network Programming guide, TLPI, and OSTEP are good resources.
Each assignment took me around ~60 hours to complete, even with prior systems courses and C/C++ exposure (no professional systems/SWE experience though). The write-ups seem like a good idea initially but end up being an annoying after thought when most of the design choices are made for you in the assignment instructions.
IMO going over the lecture videos and making sure I understood the concepts well was enough to do well in the midterm and final. The midterm and final questions mostly seemed fair. However, the instructor sort of flubbed sharing exam answers after grading. Not sure if this was a dragged-out accident/oversight or intentional (if she considers the exam questions to be for evaluation purposes only and not for students to learn their mistakes from, that's fine with me, just say that)
Worth noting, the course slack is the best I've seen in OMSCS. The TAs range from (a) being helpful to (b) going above and beyond their call of duty to (c) sometimes trolling you. I didn't really mind (c).
Rating: 4 / 5Difficulty: 4 / 5Workload: 25 hours / week
C3Idv8ylYpFDQqlwsq904A==2025-08-07T21:12:04Zfall 2024
Introduction to Theory and Practice of Bayesian StatisticsPros:
Cons: 4) Should fit a bit more material within the topic schedule/assignments to make the course slightly more challenging 5) Should add proctored closed-note exams or quizzes to hold you accountable and force you to study more.
Mixed / advice: 5) TA's notes website -- there's a few questionable code snippets or pieces of material. It's also clear the website was a work in progress. However, I recognize the TAs went way above and beyond their duty to create and update this website which ends up being way more valuable for most students than the lecture videos. For that, Aaron deserves a promotion. 6) I skipped the lecture videos so can't comment on them. I followed another reviewer's suggestion and read Student's Guide to Bayesian Statistics by Ben Lambert -- its fairly approachable, gives good intuition, lines up somewhat with the course schedule, but math-light. Therefore, I definitely recommend reading through Aaron's notes, relevant parts of other textbooks, OH notes as necessary for the assignments and general understanding.
Rating: 4 / 5Difficulty: 2 / 5Workload: 20 hours / week