100 Most Recent Reviews

  • zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:35:48Zsummer 2023

    Data Analytics in Business

    This was one of the easiest class...With that being said, I see this has a lot of low rating, but I am not so sure why. Some parts of the lesson does suck. Now that I read the reviews, I vaguely remember that marketing section exists...But I think the real take away for this class for me was how to use R.

    This class shows you line by line code on how to use R for analysis, and I learned a ton about R that introduction to modelling did not teach...other than that, I do not think there is much to this course.

    Just take the exams and do your project and you will end up with an A.

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

  • zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:31:05Zspring 2023

    Introduction to Analytics Modeling

    How to succeed: Listen and UNDERSTAND all the lesson materials. Try your best on the HW and review the answers later. This class is not a hard class, but as the name suggests, just an introduction.

    Overall, I do not feel like I wasted time, but I do not feel like I gained a lot. But I am still giving it 5 points because I got things out of the course that is exactly aligned with the name. introduction to modelling.

    I wish this class was an elective for the A track guys. As I am C track, I found this course not so relevant for my future, but who knows.

    Rating: 5 / 5Difficulty: 3 / 5Workload: 9 hours / week

  • zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:26:46Zspring 2023

    Special Topics: Business Fundamentals for Analytics

    I think this class should be an elective for the business track guys. A lot of memorizations to be done with great attention to details. The test questions are confusing. I ended up with a C, but I know for sure if I studied A is definitely possible (I am not saying this just to make myself feel better. I took DL, CDA, DO, etc and this is the only classes I have a C in. All the others I got A's...). Just take this class as one semester of annoyance and you will end up with an A. Have fun.

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

  • zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:22:24Zspring 2023

    Computing for Data Analysis: Methods and Tools

    How to succeed: Do all the homework and mock exams that they give you. Really try to understand everything that you do on the HW by searching stackoverflow, etc.

    Great introduction to a master's class. Definitely a foundation stone for the future classes you will be taking.

    I thought homework are interesting and great examples of real-life application (maybe except for the string manipulation). Yes, the strict grading programming can be little bit of pain, but they really ensure fair and quick way to make sure that you have the right answer.

    I think this course is a good measure of how you will perform in this program. It challenges your coding and logical thinking area.

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

  • zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:17:33Zspring 2023

    Data and Visual Analytics

    How to succeed: Know javscript for D3. Know SQL. Know PySpark. Then you will be good to go!

    This class is not really related to visualization. Rather, they put together a bunch of technology that is relevant for data engineering/high end data scientists.

    If you want to get something out of this class, when they are doing AWS/GCP/Databricks/SQL, with the extra time that you have study those 4 in deeeeep depth. Those 4 will be very relevant after you graduate. Other things they put emphasis on such as D3, final HW which is little bit of classical machine learning and a final project, DO NOT WASTE TIME ON THOSE. For classical machine learning, just take computational data analytics class (it is an awesome class BTW).

    Anyways, Overall this class focuses on all the wrong things and flies over the important ones. I would just spend this semester looking at the above 4 things and ignore other things. Yes you might end up with a C, but you will be much more marketable than someone who got an A and knows how to code D3.

    Rating: 2 / 5Difficulty: 3 / 5Workload: 15 hours / week

  • zPMpZ4qbv+TFymmd8hVL2g==2024-03-28T12:11:03Zfall 2023

    Deterministic Optimization

    Difficulty: If you are good at math (especially linear algebra), this course will be pretty easy. I spent about 10hr/week, but I can def see why others would spend a lot more. Tests are very tricky. So learn your concepts during HW well. the HW grades are "curved" since they are student peer graded. I ended up with an A, but barely.

    How to do well: Like I said, HW are "curved" but the tests are not. Really make sure you learn the concepts from the solutions published after the HW given. Also studying the previous tests help, which they publish fir studying materials.

    What did I learn and how was it: I did learn quite a bit. Given a situation that is deterministic, you basically learn how to model and optimize the system in various settings. The lectures and the HW are little dry, but honestly, as long as I learned in depth, that is all I cared about.

    Complaint: As other people say... HW to exam proportion is wayyyyy out of what they are supposed to be. Make sure that the HW's are TA graded, so that they are not "curved". Also HW is where the real learning happens, so students should spend a lot of time on HW (which is not the case since they are "curved") and increase the HW weights on the final grade.

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

  • aX7v+yTogLvsY6OhYBAN7A==2024-03-26T23:39:41Zspring 2023

    Game Artificial Intelligence

    Overall, it can be a good course if you are interested in gaming. If you don't, and you happen to be entering a busy period, don't take it because it will make you feel like doing something utterly tedious and useless for 70% of the time

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

  • +hHhhpWGBBONLUJ32JdYrQ==2024-03-24T03:42:56Zfall 2023

    Machine Learning

    This course at Gatech has got to be one of the worst. I can't understand why it's made mandatory, but I absolutely dread every minute of it. It's managed to sap all the enthusiasm I had for ML.

    Lectures: Honestly, most of us skip the videos altogether. We resort to YouTube or Google for explanations because the lectures are just painfully drawn out. Professor Isbell tends to go on and on, making it hard to stay engaged.

    TAs: It feels like most of them are just there for show. Their office hours are a joke. They often contradict themselves from week to week. Plus, their lack of attention to detail, whether it's organizing course materials or posting updates, is frustrating. It's like night and day compared to Joyner's courses.

    Assignments: These aren't about learning; they're about jumping through hoops to get the results they want. Understanding the requirements or grading rubric feels like trying to solve a mystery every time. It's all busywork, and the end product is just a report filled with nonsense. Your coding skills hardly matter; it's all about that damn report, focusing on making the visuals impressive and filling it with unnecessary fluff.

    Bottom line: Stay away from this course. If I could turn back time, I'd never have signed up for it. It's a surefire way to kill any interest you have in ML (or even OMSCS). Trust me, the other courses are a much more interesting compared to this nightmare.

    Rating: 1 / 5Difficulty: 5 / 5Workload: 35 hours / week

  • jsmRaN0UA8iPxGKeKcZ47Q==2024-03-22T16:06:55Zspring 2023

    Introduction to Graduate Algorithms

    This course is the worst in the entire OMSCS program (and I did 6400!). AVOID AT ALL COSTS. If it is required for your specialization, reconsider the program or the entire institution! I mean go elsewhere than GeorgiaTech for your Masters. The lectures are outdated, the professor is absent, and the TAs crew is condescending and has know-it-all attitude. This course will leave you tramautized and questioning your life choices.

    Rating: 1 / 5Difficulty: 3 / 5Workload: 20 hours / week

  • Kl6wd6HHkWJU/zR8wBs9Ww==2024-03-17T13:34:46Zfall 2023

    AI, Ethics, and Society

    Ok. This class has merit. A lot of people here hate on this class, but it has a great purpose. It is an excellent introduction to AI, Stats, and Data Analysis using Python. If you've not done any of these before, it's a great chance to learn. A lot of the criticism seems to come from right wing leaning people who are afraid the course is 'woke'. I don't think that's fair. Statistically, this class is showing when AI is biased and when it is not. Certainly many of the datasets we analyzed were not biased, and some were. If you want to approach subjects like race in an intelligent way, this class will give you the tools. The fact that some of the datasets don't show any bias or even any statisctical pattern leads to my one critique. It can be frustrating to compute figures that are meaningless. Or graph outcomes that don't follow any pattern. Do not let that discourage you. Just report the results and let them be. That's how real data works. You're not going to get a bad grade because you don't find bias. Sometimes it's not there. The assignments are sometimes a little ambiguous, but the TAs will clarify in Ed Discussion. I'm sure the TAs hate answering the same questions over again, and the course could benefit from updating the assignments a little bit.

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

  • DdWQS12tsZ78dqc1ajCb8g==2024-03-16T18:34:51Zfall 2023

    Natural Language Processing

    This is my 7th course and by far the best. It does a good balance of theory/lecture, programming assignments and paper overview. It doesn't beat you to death with sadistic assignments and provides a shell where you focus on the conncepts learned and see it working. I finally understood the intuition of a Transformer model and its variants. Prof. Riedl is stupendous and i wish he finished the entire modules.

    He should come up with advanced NLP since there is lot of interest on how to quantize and FineTune a LLM models using HITL RLHF or DPO. I think Finetuning itseld can be a course starting with multiple sub-word, PE and attention techniques that can be explored.

    Only thing that they can improve is better homework recitation by TA.

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

  • 0JpqZRdZ+5ISBWCVg9Dttw==2024-03-13T00:59:06Zfall 2023

    Artificial Intelligence

    The topics discussed in the course are very interesting. The projects do take time but you learn a lot.

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

  • Fx764/8uQ176zln00wiLog==2024-03-11T17:54:07Zfall 2023

    Digital Marketing

    I took this course along with Cognitive Science and Global Entrprenuership, and still finished this course within 3 weeks (thats right, the entire semester of work finished within 3 weeks even with two other classes going on). There are weekly discussion posts (easy peasy), a handful of short papers (easy peasy) and two exams (moderately easy). Finished with an A and was able to focus on my other classes. Content is very simple and stuff you already know if you pay attention to marking campaigns you see online. A 9th grader could make an A in this class, but I'm not mad about it.

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

  • KuOIUWmjjnFUwPKzd4UMoQ==2024-03-11T05:50:02Zfall 2023

    Artificial Intelligence

    This is the course that convinced me that reviewers on this site tend to give higher quality ratings to classes that are more difficult regardless of whether they actually deserve high scores. This academic version of Stockholm Syndrome is the only way a subpar course like this can get a 4+ rating here on OMSCentral. It's not quite as bad as the trainwreck that is GA, but at a 4+ it's so galactically overrated that it boggles the mind.

    • The workload is just too high. I don't think it's a problem to an absurd degree, but 3-credit classes should generally strive to stay under 15 hours per week of work, or certainly at least under 20. The ~25hr/wk workload is currently the 6th highest of courses reviewed from OMSCS. I'm a pretty average student and this estimate feels correct to me, but I skipped most of the extra credit and other challenge problems. Most assignments feel 20-40% longer than they should be.

    • The workload wouldn't be as bad if it was spent actually learning things, but WAY too much time is spent dealing with weird edge cases that aren't essential to understanding the algorithms. Edge cases happen in the real world, but there they can be easily debugged. The Ed discussion forums for this class were an unfortuantely essential resource since it's where other students would reverse-engineer the jank of the assignment. Ed discussion forums should be for additional help, not "the other half of the assignment instructions we just didn't tell you".

    • A very common and frustrating occurrence was when I'd pass all local tests, but fail on the Gradescope submission. I always felt completely helpless when this happened, as Gradescope was a black box that said little more than "hmm that's not the answer I was expecting". I would run through my code step by step confirming everything worked locally, but it still wouldn't pass. With non-descriptive errors, I could never be sure if Gradescope was failing due to a genuine bug on my end, some janky edge case, or an issue with Gradescope itself (which happened several times throughout the semester). Something like 25%-40% of all Ed posts were "everything works locally, but GS fails", so the problem was obviously widespread. We really need better error messages so we can at least attempt to resolve issues ourselves.

    • The TAs did not seem on the ball in Ed discussions, often replying late and with incoherent answers that were indicative of not understanding either the question being asked, the content of the assignment, or both. It was typically up to other students to get an actual answer to problems being faced, which was obviously not very reliable since it depended solely on the comraderie of others facing the same issue. I saw many questions that just went completely unanswered for 48+ hours. You want to start assignments early to account for this, but not too early so others hopefully have a chance to deal with the weird edge cases before you spend hours dealing with them yourself.

    • Ambgiuous assignment directions. It started OK with first few assignments, but context gradually declined. In some of the later assignments you're forced to implement functions with little more than a one sentence docstring. It was pretty common for me to have to work backwards from the unit test provided to figure out what was even being asked for.

    • Lots of math equations that are just theorem dumping with no examples or broader context to explain what's happening. There's very little enlightenment in much of the textbook and supplemental reading. Far too often I'd be implementing an algorithm and I'd come across a math symbol that I had absolutely no clue what it was referring to. Also, some like |E| would typically refer to the absolute value of E, but then sometimes refer to the determinant of E. Untangling this stuff can lead to more frustrating hours of debugging.

    • The lectures are very handwavy and don't even begin to prepare you for the assignments or the exams.

    • Exams have almost nothing in common with the lectures or assignments, and are very all-or-nothing. I recommend getting a digital copy of the book so you can ctrl+f

    I got an A in this course, but despite the high grade I actually learned surprisingly little.

    Rating: 2 / 5Difficulty: 5 / 5Workload: 25 hours / week

  • G3KVZGHWA4G3FwdOj9e/Tg==2024-03-10T23:48:41Zfall 2023

    Human-Computer Interaction

    I dropped this class Spring of 2024 (at the time of this writing, the latest I could choose was Fall of 2023), my fifth class in the program so far. I am putting this review here for those like me that took based off of reviews from previous semesters; this class is no longer a lighter elective level, it is a core class and should be treated with that assumption of difficulty and workload going in. The class instruction and content is still okay, but the revamped workload is absolutely brutal, I do hope it levels out because this can be a good class, I do feel that the intent to make this a “difficult, serious class” because of complaints went too far.

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

  • 2u0jxIDIVgLE3vfs00X60A==2024-03-10T22:45:48Zfall 2023

    Secure Computer Systems

    For context, my day job is doing computer security research. Pros: The course covers a wide range of topics. For example, we talked about data privacy and k-anonymity, which I didn't know much about before this class. Cons: Some (maybe half?) stuff felt completely theoretical and irrelevant to the real world. I spent a lot of time memorizing syntax and theoretical frameworks. Some of the mechanisms described were so theoretical I have no idea how they would be implemented in the real world. The choice of things to focus on was bizarre. For example, the Authentication module had no mention of public key cryptography (in fact, the course barely mentioned it at all). The entire Authentication method was about an obscure paper about hardening passwords with keystroke timing. Which is a cute idea I guess, but not commonly used. And since there was no mention of asymmetric crypto, a lot of students probably left that class thinking passwords are the only way. The quizzes and tests were very ambiguous and wanted pretty specific answers. A lot of mind reading involved. (Lots of short answer as well) Lectures were very dry. Projects were tedious.

    Ultimately, I still got an A. But this class wasn't worth it. I cannot recommend it to anyone. Either you're already familiar with this stuff, in which case you won't learn much of value. Or you're new to it, and you'll be led down a rabbit hole of irrelevancy.

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

  • PkimOgKOFN4UDCSlxgdXZw==2024-03-10T02:47:09Zspring 2023

    Machine Learning for Trading
    • Content is easy to understand.
    • Programming assignments are easy to understand (I had prior Python/Numpy/Pandas knowledge).
    • Reports require Joyner format, which made me anxious about potentially messing up my formatting.
    • Assignments take forever to grade, so you don't have any bearing on your report style/formatting until nearly halfway through the course.
    • Quizzes were open book but were a poor test of knowledge and were often more of an egg hunt for "meta" course content.

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

  • QaHiGrgd+Pjfq59R17SqTA==2024-03-07T09:36:47Zfall 2023

    Human-Computer Interaction

    It is a core course of a spec now rather than just a pure elective course. Keep that in mind,

    With the addition of HCI specialization and the complaints that students who select HCI path will great without the rigor of an OMSCS course, the boss has decided to up its difficulty so past reviews are not a good indicator for this specific course.

    Rating: 5 / 5Difficulty: 5 / 5Workload: 26 hours / week

  • yDLjYZSnL0WZX+N59lDC+A==2024-03-04T19:19:16Zfall 2023

    Introduction to Graduate Algorithms

    The class is not difficult to study as such but, its the pattern of evaluation. This feels more of an english class rather than an algorithms class. The homework assignments require you to describe the algorithm in words and then its a word play after that. The grading is highly inconsistent. Its basically I as a TA think that this should be your grade. Rubric is unknown and it suffice to say that it has been an extremely frustrating experience. If you give a different answer than what the TA is expecting, expect a major penalty.

    Rating: 1 / 5Difficulty: 2 / 5Workload: 15 hours / week

  • f5LbeClAhhqYmXvJMHwplw==2024-03-04T06:15:32Zspring 2023

    Computer Networks

    Fairly easy course. Programming assignments due every 2-3 weeks, quizzes every week, and 2 exams.

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

  • f5LbeClAhhqYmXvJMHwplw==2024-03-04T06:13:31Zspring 2023

    Software Development Process

    Amazing course if you are already a software engineer Super easy A No exams or quizzes

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

  • f5LbeClAhhqYmXvJMHwplw==2024-03-04T06:11:54Zspring 2023

    Human-Computer Interaction

    Course is for non-software engineers 6-8 page paper every week Annoying group project

    Rating: 1 / 5Difficulty: 3 / 5Workload: 12 hours / week

  • hywNQQpgkkaGNA7MIF86mg==2024-03-03T06:25:19Zfall 2023

    Introduction to Computer Vision

    Generally a pretty solid course What is good: good lecture recording, detailed walk through to fundamentals, linear algebra heavy and very beneficial. Demanding assignment to keep you engaged What is bad: Not up to date, one of the example is in one of the final project where it is trying to use CNN for recognizing door number but restricted use of YOLO for reason of "it will be too easy". This model is already the state of the art model so what in any meaning is the use of a final project that correlates nothing to the modern technology set. Heavy lecture video and very short time it left to finish the assignment. I think most of us are working, I spent Mon-Fri night trying to finish all video, each time roughly 2hrs only to find out that I need to finish a major assignment that due on Monday. On average, this leads almost 25-30 hrs of workload to my life and I literally have no time for self care, my wife is in another state, visiting her during the weekend and my schedule is hell for the following several weeks. Conclusion: worth taking, you won't regret it, but definitely a hell of a burden added to your life.

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

  • PjAwzX5S8rzvqOGfY0JbHQ==2024-03-02T19:28:34Zspring 2023

    Introduction to Computer Vision

    Such an interesting topic ruined by the worst possible set of assignments. The entire course feels like it is designed to ONLY test you to satisfy some huge egos rather than help you learn anything.

    The staff is irresponsible and could not care less if you learn as long as you're able to pass Gradescope somehow. This is my 6th course and by far the worst and the only disappointing learning experience. Highly recommend staying clear of this one but do learn CV by yourself (Prof. Aaron's lectures are great) or another course outside GaTech.

    Rating: 1 / 5Difficulty: 5 / 5Workload: 40 hours / week

  • n70rALB3Re1J2WuKho9B4A==2024-02-28T17:00:33Zfall 2023

    AI, Ethics, and Society

    My last and worst class in OMSCS. The class discusses misleading graphs, protected class and bias, which could be covered in less than a week at its depth. Instead this is a semester long course, so tons of repetitions just to fill up the time.

    This class could've easily won the most tedious assignments award in my entire academic life. The assignments all exhibit an awful pattern of high volume of low value work. For example, in one of the so-called AI/ML assignments, students are asked to calculate mean and standard deviation for a dataset, copy that into a report and format, calculate those for 10% of data, calculate those for 60% of data. Repeat for a protected class, and repeat for all subgroups in the protected class. The calculation in Python is effortless, the time is spent on copying and formatting ~30 sets of values in the report. No one checks the result as long as some numbers are there. Data analysis is minimal, as long as you wrote two sentences.

    It's surprising that this class at its current state is considered a graduate level CS course and one of the electives for II specialization. Anyone with basic coding experience, keep your sanity and stay away.

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

  • FxufW8teIQblLVszOZOWiA==2024-02-26T08:51:06Zspring 2023

    Introduction to Computer Vision

    I share the sentiments expressed in the previous reviews for the Fall 2021 term. -" WE ARE HERE TO LEARN, NOT HERE TO AMUSE THOSE IRRESPECTFUL IRRESPONSIBLE WORST DAMN TAS. "

    My background: I took computer vision courses previously, and had conducted related research in this field. I am not an expert in Computer vision, but I had indeed published peer reviewed paper in this field.

    My reviews: Our primary focus should be on learning, and unfortunately, there were significant challenges with the handling of the course by certain TAs. This has been my seventh course, following successful completion of six others with As. However, this time, the experience has been notably unfavorable.

    The communication with TAs was inadequate, as questions posed on Eddiscussion went unanswered. Additionally, I encountered issues with grading accuracy. Despite providing correct answers, corrections were refused, and instead, I experienced insulting remarks and unwarranted teasing. It was disheartening to feel disrespected and undermined in my pursuit of knowledge. Most ridiculously, our score were simply deducted for the questions that were NOT even asked. - gradings were arbitrary, if you challenged them, they would threaten you through something like academic violation!!! They are not open, once they graded, even it was wrong, you had no choice but to accept, this was actually against academic integrity - but they do not care. What they (these so-called TAs) took advantage of the authority to make you fail the course.

    After six As in my previous courses, this is my 7th course. I had the WORST experience. First, TAs did not answer your questions, many (if not all) questions were unanswered in Eddiscussion. Second, TA graded your answer obviously incorrectly but refused to get it corrected. Instead, they insulted you, and found whatever comments they could to tease you - I felt being teased like a monkey!!!

    Rating: 1 / 5Difficulty: 1 / 5Workload: 29 hours / week

  • 4kluWNzA+TUcEksL17C2JA==2024-02-18T00:48:41Zfall 2023

    Special Topics: Quantum Computing

    Good intro to QC from the CS perspective. Lectures are informative and homework and labs help reinforce the material. The one issue I had is that sometimes the problems in these assignments were not written very well and so were either confusing or outright incoherent. I suspect this will be fixed as more iterations of the course occur. Overall would still recommend despite this however.

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

  • XF7CqI5QZr6qy67/zIwMMQ==2024-02-12T20:03:39Zfall 2023

    Advanced Internet Computing Systems and Applications

    I agree with previous reviews that if you don't like writing then do not take this course. Every week you have to essentially write a 6-8 page essay and only in the final few weeks do you have to focus on the project where coding is optional to help you write the 15-20 pages.

    On the other hand, they are extremely lenient in marking. I peer-reviewed some extremely poorly written essays, but the averages were always 80+.

    The interesting part was peer reviews. Some seemed like they used ChatGBT to write it and few students gave meaningful reviews that help you improve.

    The TAs were also receptive to feedback. Students didn't like the brief one-sentence reviews from the TAs so the TAs started expanding which was nice to see.

    The exams are open book and no HonorLock is used, but they are on top of the weekly assignments.

    The topics were interesting, but the lectures had errors or were confusing at times. I feel like Data Warehousing and later concepts we didn't ingest properly due to the time constraints with the final project and final exam.

    Rating: 4 / 5Difficulty: 3 / 5Workload: 14 hours / week

  • o8HMU4BGyQsYYgXng2bOOw==2024-02-05T15:45:26Zspring 2023

    Mobile and Ubiquitous Computing

    This is it. This is the worst class in the OMSCS. Yes, I have taken Software Architecture and Design. This is worse.

    Only 30% of your grade in this class is based on your own work. The rest is based on group projects. The volume of work in these group projects is impossible to complete alone or with 1 other person unless you're unemployed and not taking any other classes. So if you get stuck on a bad team, you are completely screwed. Your grade in this class is purely a matter of luck. You'd better pray that you have the good fortune to get at least 2 team members that actually do some work. Good luck with that.

    If your teammates don't do any work, you're completely screwed. The instructor will not allow you to change teams or request additional team members. You will not be given extra time, even if you can prove that your teammates are not responsive. I literally showed Ploetz screenshots of my team members explaining why they would not be contributing to the project. He did nothing.

    Let me state this again: YOUR ACADEMIC COMPETENCE AND WORK ETHIC WILL NOT SIGNIFICANTLY IMPACT YOUR GRADE IN THIS CLASS. The work that you actually do barely matters. Your team determines your grade.

    Rating: 1 / 5Difficulty: 4 / 5Workload: 30 hours / week

  • Ki03VESWPL4A6uzTOc7CeQ==2024-02-01T11:37:24Zfall 2023

    Data and Visual Analytics

    worst class structure ever, lecture videos are totally useless

    Rating: 1 / 5Difficulty: 3 / 5Workload: 21 hours / week

  • z9NQv9C9iU8cniecUaWM/Q==2024-01-31T14:52:09Zfall 2023

    Deep Learning

    Also posted on OMSHub,org

    I took the class in Fall of 2023 as my 6th class in OMSCS.

    Overall I really enjoyed the class and got an A but just barely.

    The class consisted of 4 assignments and 1 group project project, and 5 "quizzes".

    Prerequisites:

    Python proficiency, especially comfort with numpy python package since pytorch uses a similar syntax. Familiarity with machine learning. If you don't have this, I highly recommend taking the time to do Andrew Ng's machine learning or deep learning specialization on Coursera. Assignments I had to work on the assignments almost every day. They were very hard but if you were consistently working on it, checking EDstem, and office hours you could definitely get through them and learn a lot.

    Assignment 1 + 2: Deep learning basics and Convolutional Neural Networks from Scratch. I think the most useful thing I learned was how to do back propogation by hand and getting comfortable with using Tensors in pytorch.

    Assignment 3: Shortest assignment. Style transfer and visual explanation of deeep neural networks.,

    Assignment 4: NLP basics, RNN, LSTM, and Transformer Architecture. This IMO was the most interesting assignment. Language models like ChatGPT is built on transformer architecture so understanding them is very important.

    Quizes: IMO these were more like exams and the most stressful part of the class. You absolutely need to study for them. I did about average on these but I feel like quizes don't always reflect the assignments or the lectures very well.

    Projects: Your group has to do a deep learning project. My team did a kaggle competition where we looked at an image classification task for very large images (file sizes of >1GB). Kaggle is great because they provide free GPU resources (up to a certain amount per week). The class also gives you some GPU credits on Google Cloud but it is a very limited amount. We also had to submit a 6-page paper written in Latex document which is useful for those interested in publishing their results. The grading on this was very minimal. We had to submit the assignment within a few days of the end of the class so the TAs did not grade the report that harshly.

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

  • WrKfQ1jcSrBuYU1JSI1/pA==2024-01-24T03:34:55Zfall 2023

    Machine Learning

    I absolutely loved this class.

    The lectures: I had lots of fun watching them, the two teachers made me laugh. I would recommend to read the corresponding chapters beforehand because otherwise, you might lack some helpful knowledge.

    The papers: With chatGPT, writing papers in Latex was very bearable. chatGPT use is encouraged as long as you don't just copy from it and I had some great "discussions" that would in a remote study normally not be possible. I recommend to use a Latex template - in our semester, someone provided one and that saved tons of time. The feedback that is according to the professor super important came always too late to incorporate it so don't count on it.

    The exams: I read the book once, some chapters twice when I needed to lookup some algorithm, and I tried to understand the general topic of the papers. That was enough to get between 70 and 80 % on the exams. The questions I got wrong were often formulated in a little bit of a tricky way, so take your time to understand what they're asking.

    Ed Discussions: This was one of the classes where I least used EdDiscussions. That doesn't mean there were none, but since "stealing" code is so encouraged, and ML is such a hype, most of what I needed to know I found on the web.

    Office hours: I went to the first one and found them a bit wordy - after that I never went again,

    What I would do better: I would try to organize my code better. Much of what I did, I reused over the assignments and proper modularization of functions would have saved me quite some time.

    Rating: 5 / 5Difficulty: 4 / 5Workload: 23 hours / week

  • /AO2b9uFNEUxLlYJ/EqCfQ==2024-01-21T19:40:55Zfall 2023

    Game Artificial Intelligence

    This is a good course, though pretty easy. The assignments are just really well designed and fun. I really appreciated the effort put into them as well as some of the optional activities like tournaments for some of the AI we designed. I didn't spend much time each week because I didn't have the time, but I absolutely could have sunk 40+ hours into some of the assignments and enjoyed it. Didn't have any C# experience going into this and I was fine, people who are newer (< 3 yrs) may have a bit of trouble, make sure to get intellisense running and save a ton of headache.

    The class gives you a good overview and implementations of some 'good 'ol fashioned AI' but has some opportunities to experiment with RL/DL as well. I really gained an appreciation of the difficulties of designing game AI and how different it is than what you see in ML papers.

    The downsides are that the lectures are a bit long/slow, and the class is not particularly challenging. Honestly though, I think this works as a fun class that has enough material that if you want to put in more work you absolutely can, and I'm a bit regretful that I didn't.

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

  • UxM4W8UQJZ4uBeXkBIQvBQ==2024-01-21T18:22:09Zfall 2023

    Graduate Introduction to Operating Systems

    The course material was very useful and practical. My current role is as an embedded software engineer and I have been able to put some of the principles to work right away.

    The workload for this course was much heavier than I would have expected. One good thing is that the projects are typically made available 4 weeks before their due date. Overall, I found the projects to be enjoyable, just too much work for one three-credit course.

    There is no textbook for this course, but several interesting white papers are provided as reading requirements. Most of these are more than 15 years old , but they are helpful at providing historical context with regards to operating systems and the concepts generally still apply.   Only the first half of the course actually focuses on operating systems. The second half of the course focuses on cloud and distributed computing.

    To be successful, you will need to be able to write code in C and C++. Many of the projects build upon work done in the previous projects or parts of projects. In order to avoid rewriting the same code over and over again, create abstractions and focus on implementing one piece at a time, rather than doing everything at once or using really long functions.

    Some of the projects do not directly relate to the course materials, which was frustrating. They are there to prepare students with practice and background ahead of the main portions of the project. However, everything is graded, so to succeed students need to write a lot of code.

    The TAs were generally responsive. Although sometimes they could be condescending when responding to students (many of which already have many years of work background in software development).

    There are two exams, each covering around half of the lectures. To prepare for the exams, students must review all of the lectures and white papers. The TAs do provide some crowd-sourced class notes from previous semesters, which are very helpful in preparing for exams. To prepare for exams, I would recommend taking notes as you go, reviewing all the class notes and reading the white papers highlighting any information that you think might help for the test. Some of the test questions were on obscure details from the white papers, such as recalling and comparing specific benchmark tests. Even with a lot of studying my test scores were in the low B range. Fortunately, the projects use gradescope and by starting earlier and refining their work, most students should be able to get 100% on the projects.

    Rating: 5 / 5Difficulty: 4 / 5Workload: 23 hours / week

  • exenhSmf5lOOcSoZUrQd+Q==2024-01-20T20:13:36Zfall 2023

    Human-Computer Interaction

    This course is interesting for those who want to learn more about design process. I specifically selected this course because of Prof. Joyner. Lectures are engaging but there are a lot of assigned readings that are included in the midterm and final exams. However the exams are open book so if you're short on time and miss a few papers there is 2hrs for the exams, more than enough time to search for the answer. Also note that there are 10 weekly reports due. The reports are not difficult and as long as you follow the rubric, there should be no problems getting full marks. However, other students in the course complained about inconsistencies in grading - I personally only experienced this once where the same abstract I had used for several of my M assignments was docked marks but scored 100% with every other TA.

    There was also a group assignment in Fall 2023. If you are looking for a good group, try to join up with other students who are pro-active, many students started forming groups on Ed Discussions from day 1. There are four weekly check-ins and the final submission is a 30 page report and a substantial part of the grade.

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

  • exenhSmf5lOOcSoZUrQd+Q==2024-01-20T20:04:35Zfall 2023

    Machine Learning for Trading

    The content of the course is interesting and structured well. I believe this was the first semester they had the midterm and final exams as open book/internet - iirc the only restriction was no communication with other people during the exams. The questions seemed to be much more difficult than practice qns provided (which I believe reflects the exam style of previous semesters). Exams were not difficult but the wording of qns can be confusing and a 1hr time limit to review 100+ t/f statements means that you're generally working off of knowledge and only looking up a few qns here or there. Marks were scaled for exams - exam out of 110 and students would receive the mark out of 100. Essentially disregarding 10 incorrect answers.

    As others have mentioned the assignments are extremely detail oriented. There is a long document with all the assignment requirements + a 1.5-2hr weekly TA session going over quiz answers and walking through the assignment. I got full marks on all the coding sections but lost marks on seemingly unimportant details in the report like a line color on a graph. It would be great if the auto-grader could provide more informative feedback as all the assignments are related. Receiving marks and feedback 4 weeks after submission sometimes delays progress on subsequent assignments. Start early on assignment 3 and 8 if you can, those are the most time consuming! Most other assignments required <5hrs to complete.

    I would recommend this as as starter course for OMSCS. Prof. Joyner is top notch, the content is interesting and not difficult. TAs are very responsive, make sure to keep an eye on Ed Discussions. It's a great course to ease back into studies.

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

  • y3EeS2GfivMVqxWb4JBVcA==2024-01-20T18:11:27Zfall 2023

    Human-Computer Interaction

    This course is a really good introduction to understanding the science behind how we interact with computers. However, it can be somewhat tedious. Most of the course work is writing papers. They aren't heavily scrutinized for grammatical perfection, but the content that you include does need to be correct. The provided structure for the paper in Overleaf was more than enough for me to never lose points on structure. The group project is just a rehash of your own individual work prior to it. It isn't very useful in my opinion, but adds a lot of anxiety due to group members and their behavior/effort. I ended up with an excellent group, but could've written and done the work for the entire project in a couple of weeks instead of it being spread out over 5 weeks of meetings and working with the members of the group's different schedules and input.

    Overall, the class wasn't too hard, but the pace is sometimes rough (some weeks have multiple large projects due). It was an interesting class and I say that as someone who has never done much in the realm of design or "frontend" work.

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

  • 3XCJT8ZLLwaBH3r1BaCReQ==2024-01-19T20:09:45Zfall 2023

    Introduction to Graduate Algorithms

    I was one of the students who had to retake that course because I did not do well on it the first time I took it during the Summer semester. I got 69 and ended up with a C in the class the first time. I think I made the mistake of taking that class during the summer with another class and since both classes had weekly homework assignments, it was really hard for me to manage both classes. I retook the course in the Fall as was average above 90 on the class. Unfortunately I did not do well in the last exam, but I scored enough where I did not have to take it a third time. This feel the pain of other students who had to take that class more than 2 times and honestly I can understand the frustration. When I was taking that course, one of the students wrote a long entry in our Canvas expressing their disappointment on the difficulty of the class and how it's preventing them from graduating. That student really scored bad in one of the exam and had already taken the class 2 times. I wanted to share a video where I explain the strategy I have used to pass the course https://youtu.be/JbjUhfrRcmA . Hopefully this can help other students who had to retake the course and also could be helpful for students taking the course for the first time.

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

  • XqI8jd8i1eJfCnKj2v8g+w==2024-01-13T00:13:21Zfall 2023

    Advanced Topics in Software Analysis and Testing

    The course really does a good job of illustrating that static and dynamic analysis can do a lot to ensure your software is error-proof. However, a lot of the methods are not super relevant in industry, which isn't necessarily a downside of everyone. The labs were pretty well organized but the lectures were hard to follow at times because the scope would change quickly.

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

  • oI/ZNriAtlOWVqKKTb3FUw==2024-01-10T21:20:16Zfall 2023

    Data and Visual Analytics

    Context for me, I had 0 background in programs outside Python/R/SQL, I double majored in economics/stats for undergrad and work in banking as a DS. This course is definitely a tough one to grade. On the one hand you will in fact come out learning a lot but all of it will be self taught. The lectures aren't useful at all. You have 4 homeworks and each one gets three weeks to be finished. This is definitely a fair amount of time, 10 hrs a week will get you to finish them by the time it's due. I HIGHLY recommend you start early on these HWs, as in the weekend they come out do at least 2 questions (the one worth the most and an easy one). Then the next weekend another 2 (the next biggest question and next easiest), and then finish with the final question (i think there was always only 5 questions.) You will see Python classes/functions here, you will see a lot of SQL (lite) here too and some are very tricky queries, you will see Pyspark, Tableau, and intros to amazon AWS and docker (which are musts in the data science world). But I'd say the main component of the course (maybe 40%) is D3, and that is awful, those questions will make you want to cry, 2 pages of code for a bar plot. 10 pages for an interactive visualization. That's undoubtedly the toughest part of the homeworks. I would recommend you pick you groups ASAP and before even thinking about the project agree you'll use it to help each other out in the hw (NOT AS IN PLAGARISM) but as in "hey for some reason this query works locally but in gradescope i get an error, what could it be?" or "any good resources for this D3 part" or "How can I make my graphs dots change color". Specific things like that, because a lot of the time SMALL parts of your code will be what's not letting you get full credit. Getting a good team is a must, if you feel like the group isn't helpful FIND ANOTHER GROUP. The group project is worth 50% of your grade and is the easiest part of it. Do not screw it up. A good group and good project will guarantee you at least get an 80 even if you struggle in the hws (60s/70s). I ended up getting an A in this course (actually 89.90) but it was rounded up and I bombed (40% F) one of the homework assignments (tried to do the docker/aws HW3 in one weekend, DON'T do this.) Thanks to my groups predisposition to help each other out, we did really well on the other HWs and got an A in our final report. I'd recommend taking this course AFTER Computing for Data Analytics and also Computational Data Analysis. I took all three fall 2023 and the dataviz+CDA combo whilst working full time nearly killed me, I was very stressed out. Don't do this, but you can deff pair this with an easier course (MGT or other mandatory intro courses) whilst working (probably even two if you really want to speed rush).

    Rating: 3 / 5Difficulty: 4 / 5Workload: 10 hours / week

  • oI/ZNriAtlOWVqKKTb3FUw==2024-01-10T20:55:23Zfall 2023

    Computing for Data Analysis: Methods and Tools

    Very fun course and a great introduction the the master's degree. The basic layout is you get a jupyter notebook with very great and detailed introductions in %MD for each topic, then you get some empty function which you run until you pass the functions test (already built). Midterms and final follow this exact format, all tests are open book so if you've done the HW saving a few stackoverflow links or documentation will go a long way to help you finish quicker. The exams are very fair, if you know already know python and did all the HWs I'd even say easy. However if you don't already know python it will be trickier. This course is perfect to prepare you for pair coding interviews or any coding process you might be involved in. All exams have more points awarded than are required for a 100%, so you can do something like earn 10 points out 16 possible and get an A! Overall I loved this course, if your new to programming you will learn A TON! If you already knew, it's an amazing refresher because honestly who much about dictionaries? This should be a prereq before moving onto other courses but this code will set you up well for ISYE 6740 (CDA) (highly recommend), and then both of these will set you up as best I can imagine (nothing will prepare you for D3) for CSE 6242 (dataviz). I stupidly took all three together but you can see how a good structure would've been.

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

  • oI/ZNriAtlOWVqKKTb3FUw==2024-01-10T20:45:09Zfall 2023

    Computational Data Analysis: Learning, Mining, and Computation

    This was a great course. A very strong range of algorithms which you get to code for each one (sometimes from scratch) with strong math proofs and demonstrations behind each topic. Don't stress about the math it's nothing too crazy just abstract derivatives (chains rules, log properties), Lagrangian multipliers (but the professor always goes over the math very well for each algorithm). While the course certainly covered these topics in depth, it wasn't the focus (about 20% of the points in HWs). The lectures were very well balanced in theoretical, math and practical terms. Unlike other courses where you never have to see the lecture videos, here it's an absolute must, it will help you immensely in doing the homework assignments (6 total), you also definitely should use the starter code skeletons for each topic and homework assignment. You learn about a bunch of machine learning and stats algorithms, from an overview of what they are, do and used for, to the their objective function, what they optimize and finally how to use it in Python or matlab. Course deals a lot with images, so a lot of the time you aren't working with traditional dataframes rather their favorite is a series of Yale face pictures reduced in dimensions for modelling. I had never worked with images so learning how to get rows and columns from a picture was tricky but it's not too bad if you have worked with Python before. This was my third course i took it simultaneously with DataViz and Computing for Data Analytics. Definitely a mistake you should deff take CfDA before this class and NEVER partner it up with DataViz, I got an 88 in this class which is a flat B. But I can genuinely say I learned a ton and it was worth the suffering.

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

  • gdeUwiiQMfe5MQuebmSzOg==2024-01-10T09:06:19Zfall 2023

    Human-Computer Interaction

    HCI is a well put-together course. The course is being revamped after Fall 2023 so I cannot speak to the current iteration of the course. For Fall 2023 the amount of writing was relentless, but manageable. There were weekly 6-8 page papers due for 10 consecutive weeks, an individual project, a team project, and two exams. The course material prepared one appropriately for all the assignments and exams. Dr. Joyner is a teacher and communicator at heart, which makes his course a pleasure to take. I was able to use concepts learned in the class in my day-to-day work immediately.

    Rating: 5 / 5Difficulty: 3 / 5Workload: 9 hours / week

  • C17orAyBtVbxMT3SftseFw==2024-01-10T07:30:18Zfall 2023

    Computer Networks

    I just took this class in Fall 2023, and received an A. This is one of the most straightforward classes in OMSCS. Take good notes. Do all of the quizzes. Spend time on the programming assignments, and make sure you really read what the TAs are asking for. Ask for help from your fellow students in either EdStem or the Discord channel (unofficial, but usually a student starts one every term).

    This class used to be completely different content altogether (used to be Udemy based) when I started with OMSCS, which is why I initially skipped it. However, Professor Konte did a great job of revamping the lecture content, and making it more modern to what OMSCS standards are like today. I only wish I had taken this course earlier in my OMSCS schedule.

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

  • Phh7FsZIJClc3m8RyfXSqw==2024-01-10T04:15:02Zfall 2023

    Introduction to Information Security

    1st class taken with a 93% (I had 100s on the first 5 assignments, so the last 2 I basically did the bare minimum to secure an A)

    This course is... interesting.

    "You get out what you put in" is somewhat accurate. Besides 1 or 2 flags, the only way for you to get a flag is to actually learn something. Problem is, learning something can only really be done by self-study. There isn't any lectures or instruction besides links to Research Paper's (which no-one is reading let's be real).

    Office hours are mostly there to poke fun at you, as the TA's are riddle master's narrowly avoiding answering any of your questions.

    The ONLY way to learn anything directly from this class is those ED Discussions. Those guys are troopers pointing you to articles that will teach you what you need, but it's still quite difficult to realize what it is you're looking for.

    Can't say my SWE career will really use any of this, but it was interesting. Not sure I'm the best person to write one of these as I'm grade focused not learning focused, but that being said I still managed to learn a decent bit from this (Wish I could have learned more about the TCP/IP stuff, but alas)

    To be clear, this class isn't hard. I'm sure there are far more time consuming and difficult concept classes. My gripe with this class is if there was maybe a 30 minute video explaining things, the workload would really only drop to about 3 hours a week. Majority of time spent is basically research rather than studying the material.

    If you are good at Ctrl+f and really know how to google specific things, I'd say about 7-8 hours a week, assuming you just want to finish the assignments.

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

  • +jTAa/Y57/V1BqtRCslpQA==2024-01-09T06:09:03Zfall 2023

    Special Topics: Business Fundamentals for Analytics

    Very basic course. The lectures and video conferences cover all the material. I took it along with CDA and was strapped for time so ended up doing last minute preparations for most of the exams. I think the topics were interesting and very applicable if you're inclined to any kind of bussiness. But the structure of the exams was quite annoying and had to memorize a lot of theoretical knowledge.

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

  • +jTAa/Y57/V1BqtRCslpQA==2024-01-09T06:03:38Zfall 2023

    Computational Data Analysis: Learning, Mining, and Computation

    Great course! I think it's a must! Great intro to various ML applications. I combined it with MGT 8803 which made the time commiment restricted. I wish i had more bandwidth to provide towards this course. It was a tough course for me because of lack of bandwidth but overall a great course. Make sure to find a good teammate for the project.

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

  • vsibVbdFfYHQ84sN6cGhvw==2024-01-09T03:53:55Zfall 2023

    Mobile and Ubiquitous Computing

    Overall, I would not recommend this class. If you are in the HCI specialization, then prepare for a below average experience.

    The course content is comprised of 4 professors teaching the subject. The course material is relatively straight-forward and the content/reading checks are incredibly easy. There are unlimited attempts so retaking them in encouraged. This will amount to your overall participation grade. No issues here.

    The communication of the TA's/mentors running this course is where the course became pretty frustrating. A bit more on that in a moment.

    The other issue is that there is a project which is worth more than 50% of your overall grade. I believe the group you are assigned will be the deciding factor in your attitude towards the class. You are able to choose the members of your group so find a good group early on. This is my most valuable advice to you. If you are put into a poor group, you will end up doing all the work unsure of your progress at all times due to inconsistent feedback from the project mentors.

    It seems that Professor Starner does not give his TA's a full understanding of the project requirements (either that or there is some miscommunication) as we were at odds with our assigned TA multiple times making the project absolutely unbearable at times. Literal weeks were wasted going back and forth trying to get an understanding of our goals and what we could and could not do.

    The final exam is easy if you study as the content is not difficult. There was one assignment which required an Arduino board. That was actually very fun and my favorite part of the course. In that moment, it felt like a ubiquitous computing course learning about sensors and making actual circuits. Only caveat is that it would be great if you didn't have to buy a $45 dollar kit only to use it once and never touch it again. If I could go back in time, I would center my final project around the Arduino somehow.

    Some TA's were great. Answering as much as they can and trying to organize as best as they could with what they were given.

    It's not that I did not learn anything, it's that my memory of this class is tarnished due to what could have been avoidable if more care was taken into organizing the class.

    Rating: 1 / 5Difficulty: 3 / 5Workload: 20 hours / week

  • ScojLJabnNDE3kGW4WxudA==2024-01-07T10:22:54Zfall 2023

    Game Artificial Intelligence

    Class is pretty hard, but manageable. Some of the homework was difficult to me for a person without too much AI experience, Lectures do follow homework assignments, but still a struggle. I had to go to TA office hours in order to get past some blockers, and some homework assignments clicked much easier than others. The good thing is that the second half of the semester is super easy, this class is very frontloaded in the workload and difficulty. By the time you finish the mid-term, you have done all of the hard assignments, and the mid-term is significantly harder than the final. I got around a 50 on the mid-term and an 80 on the final.

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

  • fpnO7tAo5zfo/9+lngZ9pA==2024-01-07T01:34:15Zfall 2023

    Artificial Intelligence Techniques for Robotics

    This was my first course in OMSCS, and I felt it was a very good introduction. I got an A in this course, and enjoyed the course, with content including Kalman filters, kinematic bicycle motion (essentially some trig), histogram filters, particle filters, some search algorithms (policy, A*, etc.), SLAM. Would definitely recommend this class, especially as a first class. Exams are closed book, closed notes, straightforward, and not too difficult; I only spent a moderate amount of time to study for the exams.

    Some points that I noticed:

    • Lectures were engaging and informative, focusing on concepts and code application.
    • Course is not overwhelmingly challenging.
    • Projects were fun and a great application of lecture material, while not being overly tedious or difficult. Some of the code can also be referenced from the lecture material, which provides a good starting point.
    • Excellent TA's, the tutorial lectures by TA Chris were very helpful to get an intuitive understanding of the material and tips for application going beyond lecture material.
    • Lots of tweaking of certain parameters to achieve high marks on assignments.
    • Potentially slightly outdated content, covering more traditional AI robotic topics.
    • Lectures alone will probably not be as helpful as the problem set lectures and TA Chris's tutorial lectures.

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

  • B7WyA0LDsvtWNQG6EZea/Q==2024-01-06T14:51:19Zfall 2023

    Machine Learning for Trading

    Summary: Overall, this course was just fine. If you're like me and just want an introduction to ML and have no ML background, then this is a great course. However, it is taught like an undergraduate course, so there is less personal responsibility, but the "coddling" takes up some unnecessary time.

    Pros: -Great intro to ML -Easy, material isn't difficult to understand -Gives some good introductory material to ML and Finance

    Cons: -Project and exam expectations are distributed across Ed discussion, weekly recorded office hours, and the course website. You need to read all of these very thoroughly to understand the expectations of the projects. -The reports are sometimes tedious. These aren't too bad but they take a lot of time and don't add much more value than a question/answer worksheet would.

    Rating: 4 / 5Difficulty: 3 / 5Workload: 16 hours / week

  • uQnc1fPbJtUhBS/I6DF3IA==2024-01-06T04:01:13Zfall 2023

    High Performance Computing

    This was my 7th class in the OMSCS. It's got one of the best course material I've done, and I feel like I'm a much better engineer for it. The labs are hard but interesting and I definitely feel like I grok the concepts well afterwards.

    Unfortunately the teaching staff were the worst I've had. I found them generally disdainful of the students, and my understanding of HPC was not aided by them. As an example, a student asked how a particular approach could possibly be efficient, given the overhead it requires. The response was "It's magic :-)", and no further elaboration.

    As mentioned, the labs were difficult - I spent the entirety of several weekends on each one, and some performance tuning in the week when I could fit it in around my job. That being said, I was able to get good marks on them so it felt like the hard work paid off. There was also an extra credit project (which used to be a normal lab). The performance of your code is a significant part of the marks, which isn't surprising of a class called "High Performance Computing".

    The midterm and exam were both very difficult. The midterm had a median of 46% and the final had a median of 45%. Both got flat grade adjustments. I didn't feel like they were good assessments of my understanding of the course material, and the instructors admitted that some questions were ambiguously expressed (which I read as "poorly written").

    The course material has been shuffled around a bit since it was recorded, so I found there was some jumping around required to have the required background for the labs.

    Overall, I'm glad I took this course as I feel like a better engineer now than at the start of the semester.

    Rating: 4 / 5Difficulty: 5 / 5Workload: 16 hours / week

  • OurjdTA81rEMUz00lB7TeA==2024-01-06T01:23:06Zfall 2023

    Special Topics: Quantum Computing

    Lectures are all purely theoretical and assignments are all purely application based. Exams ask questions never covered in any lecture or assignment, particularly stepping through examples and doing math of certain algorithms never covered in details. I wish the lectures covered more practical applications (like what the assignments are) and then have the assignments be more difficult versions but having the simple examples to work off of. They never show how to code in qiskit or how it connects to theory, but I wish they did. Other than that qualm, the course teaches a vast swath of new information so if you work and don’t have time to watch youtube videos and read the textbook, retaining information is difficult. I would analogize this course to an intro to digital logic course in undergrad in terms of newness of material and the fundamentals of logic and algorithms it teaches. The theory taught on a high level is good. Error messages in grad scope for coding assignments are awful. TAs are pretty rough with grading reports. Definitely recommend this course for the content if you’re at all interested in quantum computing and the potential power it holds.

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

  • OurjdTA81rEMUz00lB7TeA==2024-01-06T00:33:16Zfall 2023

    Machine Learning for Trading

    This course has potential but it is not executed very well. They could focus on more applicable concepts pertaining to quant trading (how to construct software systems to execute automatic trades, options theory, types of trading strategies, and so much more that they should’ve covered but didn’t). The graders are incredibly tough and will mark off points just because they feel like it when the work you’ve provided is perfectly sufficient. This course opened my eyes to nothing and taught me nothing I didn’t already know from other ML courses (the ML they teach us also taught poorly) and rudimentary research. Do not recommend this course.

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

  • mPajKgOPQ6pV3UfAxJIgog==2024-01-05T21:00:11Zspring 2023

    Machine Learning for Trading

    An incredibly interesting class. Honestly I cannot find it in another place. The only downside is that they caught me copying from another github for like 20 lines of matplotlib code and flagged me with a 0 and a warning. Be careful! The professor is nice and smart. Highly recommend to any new comers. Keep up the great shit!

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

  • mPajKgOPQ6pV3UfAxJIgog==2024-01-05T20:57:17Zsummer 2023

    Deep Learning

    A well delivered class. Doctor Zsolt is a rigorous and curious person who inspires his students to be the same. Highly recommend.

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

  • mPajKgOPQ6pV3UfAxJIgog==2024-01-05T20:54:13Zfall 2023

    AI, Ethics, and Society

    A solid class that introduces AI fairness. The class is fairly loose compared to a hard science class. Most questions are open ended and common sense based. People are nice, and I run into people with a wide range of experience some who has no coding experience and relies on Excel.

    If you are woke, you are going to love this class.

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

  • mPajKgOPQ6pV3UfAxJIgog==2024-01-05T20:43:54Zfall 2023

    Machine Learning

    This class is designed to be self-research/exploration-oriented with way too little feedback. The curve is huge, and a final score of about 75-ish gets you an A.

    There is no proof nor mathematics required but instead mostly empirical and anecdotal "project analysis". I am asked to take a small dataset so that a variety of classifiers can run on it and are asked to document findings.

    If you like tuning parameters and writing reports, this is a great class for you. I personally do not enjoy the time spent exploring topics like randomized optimization like a field miner for hours tuning parameters and instead, limited time spent per project to 10 hours. These reports make up 50% of grade.

    Anyway, there is much creativity and excitement in the field when big data meets large-scale modeling, and I believe this class shows the extent of how arduous the task of parameter tuning can be. This class's shear volume of mechanical work did not inspire me at all. I took the deep learning class first and enjoyed Zsolt's much more.

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

  • GfEI76O5YG7mruvuhEdVTA==2024-01-05T19:41:43Zfall 2023

    Human-Computer Interaction

    HCI was my first course in the OMSCS program. Overall, it was a good experience. I was able to transition into study mode (after about 5 years having completed undergrad) well with this course.

    Plusses:

    • Not very complex, fairly easy to understand
    • Assignments have clear instructions. Follow the instructions, and you can earn a good score
    • Lectures are fairly engaging
    • Some concepts covered in this course are relevant to my work as a full-stack developer
    • No coding at all (plus for me since I wanted to start OMSCS with something that wasn't very coding-heavy)
    • JDF format was actually useful in presenting the papers (use Overflow for best results!)
    • Prof. Joyner and the TAs ran the course well overall - no hiccups, good communique, clear explanation if some of your points are decked, etc.
    • Needed to come up with some design mock-ups, which I found to be fun!

    Deltas:

    • Too much workload, in the sense that it's more than required. There's 10 weekly assignments, followed by an individual project and then a team project. I felt I was redoing a lot of the work that was already done earlier in the semester again. Some aspects of these projects & assignments felt redundant to me.
    • Some concepts in this course felt more like "nice-to-know", rather than "this-is-helpful-to-advance-my-career!".
    • Peer reviews took substantial time, and it didn't feel like it was really helping either me, or my peers who gave me feedback/received feedback from me.
    • No coding at all!
    • Some of the assignments could be better elaborated on (needed to get manual answers on Ed forums to doubts that arose out of assignment questions). This could have been easily eliminated had there been more clear instructions on the question itself.

    Challenges:

    • Initially, coming up with ideas for questions asked was definitely a challenge. Over time, I was able to figure out patterns of examples and solutions to provide in assignment answers.
    • Realising that while solving assignments, no idea is stupid - it all depends on how well you present it/ defend it.
    • Tests: Preparing for tests took about 7-8 hours overall for either test, which was reasonable. Some of the questions were tricky though. (FYI, test is everything open, including ChatGPT access)
    • READINGS: this was by far the toughest in the course - lots of content to cover per week. Remembering what you read is even tougher. Better results if you just do a ctrl+f in exams instead.
    • Assignments: If you have decent writing skills, typing speed, and can keep to assignment instructions, you can fairly easily complete these weekly tasks. Otherwise, it can certainly drain a lot of your time and energy.

    All in all, it was a good course. Learnt some interesting content from this course. Especially if you're just joining OMSCS, Prof. Joyner makes it easy. Some aspects required creative work, and was fun. Some aspects of the course felt a bit unnecessary and stretched. Recommended to take if you have interest in front-end design, UI/UX.

    Rating: 4 / 5Difficulty: 2 / 5Workload: 7 hours / week

  • MITT90Jk7lR9Vp789536rQ==2024-01-05T14:14:55Zfall 2023

    High Performance Computing

    I'm surprised this course is rated as highly as it is.

    I think the content that's attempted to be taught, parallel algorithms on distributed systems, is very rewarding but the video lectures do an extremely poor job of actually conveying the material if you don't at least have a bit of background in each specific topic. From the very first topic of "Intro to work-span model", I felt like a lot of introductory material was skipped and assumed student has the knowledge of. The worst part about this is, there isn't a proper textbook for the course that has the topics covered, and the TA's and instructor don't know of any external resources to refer you to learn the material on your own time. All in all, interesting material, very poorly taught.

    Another huge con of this course is there is close to ZERO engagement in ed discussions. Nobody cares. The professor is not involved with the course at all, even though he's an active professor at Georgia Tech. The instructor is low energy and condescending and doesn't care at all. Some of the TA's are a bit more engaged, shout out to Hal Elrod, but overall the engagement is the worst that I've seen in the program by a long shot.

    Assignments are good and rewarding, and not too time-consuming. Make sure you've figured out how to set up the virtual machine and you've got it working early on in the course.

    Rating: 2 / 5Difficulty: 5 / 5Workload: 15 hours / week

  • /5OWkDXxV/LpMaGSfWIvWQ==2024-01-05T13:04:06Zfall 2023

    Game Artificial Intelligence

    Easily my favorite class of the program so far (this was my third). Game AI is such an incredible in depth, interesting dive into the world of not only game programming, but also a great introduction to Artificial Intelligence for those (like me) who had not worked in the AI field of computing before. The Professor is by far and away the most available, and active, of the OMSCS program thus far.

    I definitely felt like I walked away from the course with a solid grasp on AI fundamentals that would allow me to succeed in other AI classes going forward, but the fact those AI fundamentals were taught through fun, interesting projects in game development, made the process of learning those fundamentals that much more fun!

    That being said, a big caveat here: if you have not had any prior AI or Game Dev experience (like me!), this class can initially be very intimidating. This class dives right in, and while it doesn’t explicitly mention any AI or Game Dev prerequisite experience, I’d recommend at least coming in with some knowledge of one or the other. Otherwise, you will (like me) find some of the initial projects to be very difficult, as you try and navigate how certain tasks can be implemented in a game environment, while also trying to develop the algorithms around that task.

    If you do not come in with prior AI or Game Dev experience, I would say to expect your weekly load to increase 5-7 hours (avg. 17-20 hrs), just because you will need to catch up on some of those fundamentals. I still ended up with an A in the class, but definitely had to work for that grade.

    Finally, while the 9 projects are generally all enjoyable, some are definitely better than others. Project 9, especially, felt like somewhat of a let down when compared to the others (much less code heavy than the first 8, and not as tangentially related to the material). It’s also important to note that the projects, while released in batches early, have their due dates come at you in rapid succession. I’d definitely recommend starting each project as early as possible, as some projects are due within 7 days of one another, and some projects are certainly heavier lifts than others. Also, keep in mind for the midterm and final that you will want very organized notes (they are open book), as these tests are much less recall based, and more “how would you do this thing I’m asking you to do.”

    All in all, amazing course, and highly recommended!

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

  • FLEs0wB7jB5Q0h2c2vuOvg==2024-01-04T20:25:48Zfall 2023

    Software Development Process

    I learned some great things, and there are some great things about this course, but in the end...

    I had an A (94.5%) in this course up until the last couple weeks, when they graded A6 (got 20/100, which is completely messed up). *NOTE: Staff will not give rubric, which means there are very critical things, like certain aspects of an assignment being pass or fail...needing to pass every single test, and they will not tell you (this is flat out bad practice, IMO).

    *Then, I was doing great on my final individual proj, but then right in the very end, when I still had a chance to get at least a B (80%), there was a part (1 of 4 parts) where they gave me a 10/100. I couldn't even believe it. So, I ended up with a 77, and now I'm switching to another program. Hopefully, it doesn't happen to anyone else.

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

  • R8l0R4kwK0XSbtOW5wjGEQ==2024-01-04T20:04:27Zfall 2023

    Network Science: Methods and Applications

    Overall this course is a good intro to network science and sets you up some of the basics of the field. I thought some of the material was quite interesting, though felt that it could be a really valuable course if it dived in deeper to more applications and graph neural networks (since it's one of the ML specialization courses). It felt a bit too high level and cursory at many points. For example the neural network applications was only in one lecture in the final week of the course, with no hands on work in the final assignment.

    This course is by far the easiest one I've taken in OMSCS. The weekly readings are pretty easy to fly through, and the quizzes are 5-7 questions untimed. There are only 5 assignments throughout the course that didn't take more than a few hours each, and no exams. The assignments themselves should be improved to be more interesting and more application focused. As is, they simply let you practice some of the concepts from the lecture and get you familiar with the networkx package. However, they're at least very straightforward and don't require much clarification, and for the most part were graded fairly. Some TAs seemed to be a bit more nitpicky with their grading such as with formatting plots, labels etc.

    Rating: 3 / 5Difficulty: 1 / 5Workload: 4 hours / week

  • ZeM0UXNY1NJrZY00h0Ehzw==2024-01-04T03:51:21Zfall 2023

    Machine Learning for Trading

    Having machine learning knowledge from undergraduate, the class was not hard at all. The course achieves what it sets out to. You do not finish the class with knowledge of how to make a profit in the stock market. However, you finish with the knowledge of what is needed to make a profit in the stock market. A lot of people (like the clown below me) rates the course 1/5 because they forget it's "machine learning for trading" and not just "machine learning". Don't come into this class expecting to be Andrew Ng making a million dollars from the stock market - it's an intro course.

    Rating: 4 / 5Difficulty: 1 / 5Workload: 7 hours / week

  • ZeM0UXNY1NJrZY00h0Ehzw==2024-01-04T03:19:30Zfall 2023

    High Performance Computing

    Grade distributions for this semester were 81 for an "A", 71 for a "B", 61 for a "C". The midterm and final were both tough - even more so than the assignments. TAs were really nice. I agree with the general sentiment of the other reviews. I gave it a 5/5 difficulty instead of a 4/5 difficulty because I couldn't figure out the extra credit lab. Fully recommend to any other masochists.

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

  • zyMvNSLna3bljjRku1nfEQ==2024-01-03T19:35:39Zfall 2023

    Introduction to Graduate Algorithms

    By far the worst course in the program. Avoid it if you can. Teaching team does not accept feedback very well.

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

  • /jkAyr95FCOXJsVSa6yI6A==2024-01-03T07:18:35Zfall 2023

    Distributed Computing

    My background: 7-11 YoE full-time SWE, CS undergrad, married no kids, took 7210 as my 1st course, ended with an A.

    Lectures: I found it hard to keep myself engaged with the video materials, I feel that they were just not very mentally stimulating to me. Also, I feel that they covered too many topics, the things discussed were just at the surface level.

    Labs: This was where I mostly learned from the course. The labs were quite enjoyable and frustrating at the same time. I wished it would have been updated with the current version of dslabs as it has some improvements, there were times where I have to consult the current version to understand the framework more. The labs do not have much connection to the lectures though, sadly, and the labs could be done independently outside the course if one desires. While I don't have any issues with the labs schedule, I think it could be tweaked so that the first 3 labs were done in just 3-4 weeks or it could be made such that everything is released at the beginning.

    Exams: I share the same sentiment with the reviews here, I don't feel that the exams were fair. They were designed to trick you with ambiguous wordings.

    Time spent: lab 1 - 3 hours, lab 2 - 5 hours, lab 3 - 12 hours, lab 4 - 45 hours, lab 5 - 20 hours, lectures - 34 hours, exam prep - 3 hours

    Overall, I didn't learn as much as I expected out of this course, I wouldn't recommend taking this course to learn about distributed systems -- just do the dslabs at your own time and read software engineering publications from big tech companies.

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

  • UuztNb7E+SDCxLpV7Dd3Kw==2024-01-03T01:20:33Zfall 2023

    Special Topics: Introduction to Computer Law

    Not sure if this class has changed in recent semester, but I'd say the workload was around 10 hours/week. Leaving this review to balance out the 2 hour ones. I'd say its similar to taken Computer Networks, ML4T, and InfoSec in terms of workload.

    Still a great class. I learned a lot and I felt like there was enough material where one could dive in and spend extra time if needed. I'd only recommend if you are interested in the topic though as I dont think this class would really be helpful in a traditional SWE career. If you are looking for an easy class, there are just as easy classes that are helpful.

    I took it because i'm interested in the field and it was great

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

  • BAnd/PHOk18yCIGKIt8pUA==2024-01-02T18:58:44Zfall 2023

    Advanced Operating Systems

    Note for those looking: In Fall 2023 there was no curve at all, 90+ was an A (though they did round up).

    I think the tests have gotten progressively easier which has allowed them to do away with the curve, so keep that in mind during the course.

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

  • C5M24ecaa3kIkNM5NhgpPA==2024-01-02T15:56:00Zfall 2023

    High Performance Computing

    Great course, definitely recommend it.

    The exams and the projects are challenging but very rewarding. The best part of this course is that the evaluation components are well-spaced out. At any given point, there is only one thing to focus on.

    The lectures are good, the TAs are very responsive. The projects are very rewarding and extremely doable despite the difficulty. The projects usually have a correctness and performance component. Getting the correctness is definitely doable. You can budget your time on getting the performance right based on other priorities. The exams are difficult, but there is generous free credit and the grading is generously curved. In our semester, 82 was the cut-off for an A.

    You will be exposed to parallel programming technologies like OpenMP, MPI, and CUDA. This course taught me techniques to think in parallel and extract performance from my code. Made me a better coder since it forces you to understand the concepts of parallelism and look into the lower-level details to improve performance.

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

  • j+rann0g9HeMxS4wpX+LXw==2023-12-31T18:15:55Zfall 2023

    Computing for Data Analysis: Methods and Tools

    I had completed a couple of online python courses and a data analytics tools bootcamp in the years before I took this class, and with that background, the technical coding aspects of the course were not too difficult. However, I learned quite a bit in this class, especially about actually thinking through and implementing basic algorithms and other data analytics methods in Python. I think the structure of this class is superior due to the abundance of independent practice problems, the scaffolding of video instruction and Jupyter notebook notes, and the supplementary resources for additional reinforcement. If all graduate courses were structured like this one, I think that online learning could really become a viable new standard. For me, it was perfect for independent study and I therefore highly recommend this course

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

  • QaHiGrgd+Pjfq59R17SqTA==2023-12-31T02:14:22Zfall 2023

    Special Topics: Applied Natural Language Processing

    The easy reviews are actually CS 7650 OMSCS version of NLP because OMSCentraI doesn't seem to have them (so pls try OMSHub).

    ANLP is really a challenging course. Not only you need to learn the nuts and bolts (so please do well in CSE 6040, CDA and one of AI/RL/DL), you are also challenged on timed quizzes and learning how to make GPT.

    But you'll be well-rewarded.

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

  • tiCeFqq+i+v2bZk+eIR6CA==2023-12-30T00:16:51Zspring 2023

    Special Topics: Introduction to Computer Law

    Amazing class, could not recommend it enough. The weekly lectures with quiz associated were very fair, and you will do well as long as you pay enough attention and take notes. The two projects were also very fair and interesting in their own right, allowing you to express your knowledge and interests in connection with the course materials. The amount of work every week is extremely reasonable, unlike other courses in this program that I feel saddle you with work just because. The videos are all extremely high quality and very entertaining, and I see myself referring back to them for years to come.

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

  • tiCeFqq+i+v2bZk+eIR6CA==2023-12-30T00:13:12Zfall 2023

    Knowledge-Based AI

    My background: I am a software engineer with a few years experience, and I have a BS in Computer Science.

    In general, this class was fairly interesting, but nothing too exciting or challenging. The main course project revolves around solving problems in "Ravens Progressive Matrices" (those IQ test type questions with 3 images, and you have to solve for the fourth one). This project was fun to screw around with, but it was not really related to any of the lectures. I was able to get ~70/96 by just screwing around with my own heuristics and not reading any papers that others were using to score higher.

    The lectures themselves were pretty interesting and well organized. I wish I spent more time just listening to them for fun, but as I said it was hard to apply most of them to the main RPM project. There were six mini coding projects that related to the course materials that were pretty interesting and not too hard. There were some written homeworks as well which were more annoying than anything, but they were fine.

    There was a lot of drama related to participation, but in my opinion it was extremely easy to get a high participation grade by just doing the reviews at the beginning of the week, requesting extra in the beginning, and posting on the forum for extra points. I got way more than the max points without even really thinking about it.

    Every assignment involved a ton of writing, but it was never really that painful to get the assignments out. A lot of people had anxiety about how to score all of the points, but if you just read what they want you'll probably get most of the points.

    The exam was kind of annoying, since it was worded confusingly, but we were allowed to use the course videos, transcripts, and Google/ChatGPT as well, so they were not too bad.

    In the end, I assume most people taking this class are doing it for the interactive intelligence specialty, and don't really care about this review. But for what it's worth, the class was decently interesting, and not too hard even though it kept me busy. I'd recommend it for someone who wants to learn Python or for someone interested in cognition and how cognition concepts are applied with code.

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

  • daz28+ORmWoNJLdWj/90Uw==2023-12-29T21:12:42Zfall 2023

    Mobile and Ubiquitous Computing

    Embarrassingly unprofessional, pointless, and not up to the OMSCS standard.

    Full disclosure: I have already received my grade and got a high ‘A’ in this course. I learned absolutely nothing of any real importance, and I hate every moment of this course from week 1.

    I was going to be diplomatic in my review here, but Thad’s only real involvement in the online portion of this course was to show up twice a semester (via email) talking about catching cheaters in a smug and gleeful manner. I wish he displayed the same level of enthusiasm for this course running smoothly as he did catching academic integrity violations. Since both Thad and his TA’s can’t bother to be professional, I won’t either. The content is mostly irrelevant and only exists to stoke the egos of the four professors running it. That’s right, four professors, this is a common property problem where (apparently) nobody thinks its their job to fix the course administration.

    This course opened at 2:00 PM on Friday of add/drop week. Stuff happens, but let's not pretend a 30 second email could not have been sent out on Monday/Tuesday informing the class of what was going on. I did not enjoy refreshing canvas all week to see if it finally showed up. I even checked my registration to see if I had been accidentally dropped from the course. That gave online students 2 hours, during the workday, to look at the course before it would count as a 'W' to leave it. That is about the level of respect that you can expect from Thad throughout the course. I get the sense that the professors are used to younger undergrads, where lazy administration can be hand-waved away as a learning experience when it's really just unprofessional. They adopt a "wait and see" attitude that can work on-campus and in person but does not work for online courses.

    The TAs do not seem to be getting much (if any) guidance from him, nor does anyone care to even adjust the assignment asks to make sense for OMSCS. There is an attitude from the course runners that would be appropriate to undergraduate students, but not really working professionals (lots of pretending bad directions and poorly constructed assignments are a ‘learning experience’). The full-time student TA did not seem to understand that he is dealing with adults working full-time jobs in addition to this degree, which makes sense since he is on-campus and shouldn’t even be involved in the OMSCS section. Frankly, most of us are busier than you are, and your lack of professionalism does not go unnoticed. I don't expect an early-twenties TA without real work experience to understand that, but Thad should be embarrassed by how poorly this course is run.

    If you want the only learning I got out of this class without having to take it, go download “phyphox” and mess around with your phone sensors. The rest of the course content is hidden in cringe back-and-forth lectures that add nothing at best and are difficult to watch at worst. Also, don’t expect any PDF or PowerPoint lecture summaries. Most of the exercises are busy-work that seem to be there just to kill an in-person lecture period, so the instructor doesn't have to actually instruct. Lots of "turn the person next you and do this worksheet" type of work. This would be welcomed in-person but devolves to pointless busy work needing to coordinate random groups with very little notice. We had almost no time (2-3 workdays) between the first group being formed (by the instructors) and the due date on Sunday night. Insane… They spend 2 weeks of lectures on the completely failed Google Glass, like it is 2015 and it might have meant something. If this wasn't the egotistical professor’s pet project it would probably be a 15 minute note on the subject.

    I do NOT recommend you take this class unless you are in the HCI specialization, and in that case, prepare for it to suck. My group and I could not wait for this to be over, it was a complete waste of time and massive stressor to deal with a poorly administered class. Georgia Tech needs to get involved and force Starner (and the other 3) to actually make a full online version and split out the sections. Let me repeat that: they cannot be bothered to separate online and in-person. Several exercises require you to get a partner from the class, this is just incredibly lazy for an online class. I do not want to have to involve friends/family because you cannot be bothered to adjust this course to being actually online. This is a “cool” exercise that would be easily completed in an in-person lab session, but it is a needlessly tedious waste of time online.

    You are a second-class citizen to the on-campus people, none of the assignment instructions are even edited to acknowledge online is a thing. Be prepared to have to clarify a lot of questions about grading/assignments/dates that should have been spelled out in the syllabus on the first day. Requirements are inadvertently hidden from online students until someone asks a clarifying question. You have to ask constant clarifying questions and get TA pushback for your trouble. Hey TAs: put the details in the assignment if you don’t like answering “repeat” questions.

    Don't expect the hardware assignment to have any lectures pertaining to it, they give the history of Google Glass instead, go figure it out on your own and enjoy doing so remotely without being able to even touch the same hardware as your groupmates. Presumably, they discuss this in detail in the on-campus classes since the assignment description has this in it: "We will go through the first task together during the first session. Make sure that you don't get stuck for long at any place and feel free to ask for help. The TAs will be around all the time. Happy tinkering!" First, what session? Second, don't get stuck for too long? You gave us barely a week to do a hardware project with people we can't even meet with. And it was due at the same time as the OTHER group project, because that one needed to be pushed back since they couldn’t be bothered to even start the class on time. Then they had to redo the groups, because 15% of the class had withdrawn by week 2. Somehow, this was a surprise to the professor, which indicates that they really are not aware of how poorly this course is administered online. I assume some of those withdrawals would have been simple drops (and other people could have added), but we were only given 2 hours during the workday to make that assessment due to the late start.

    The obvious solution here is to have one professor do an on-campus section and have another professor do the OMSCS section for each semester. There is a high cognitive load on the student to even know what is due and when since there is no respect for the asynchronous nature of an online course.

    By far, this is the laziest administrated course I've taken in both OMSCS (this is my last class). I was really hoping they would have fixed most of these problems in the previous 4-5 semesters that this course has been offered online, they have not. Stop expecting TAs to meet weekly with a half-dozen groups of online students in addition to on-campus groups. This is a complete failure of course processes if that is even necessary. Define requirements thoroughly and put it in a PDF like every other course, this is amateur hour of course administration. There's no excuse because they could have and should have involved their OMS professor peers here, there is no need to do this in a vacuum.

    Please go talk to Dr. Joyner and figure out how poorly you’re doing compared to your peers. It's ridiculous how misaligned this course is, and I'm surprised it was even allowed to be offered in this current state. Thad was surprised by a lack of online office hour attendees, we all work full-time, we're as busy or busier than you and the full-time students by the very nature of this program. We don't need you to hold our hands in office hours, we need you to do your job and design projects that can be completed remotely without constant professor/TA involvement. If we're lucky the professors will receive a slap on the wrist by the College of Computing/OMSCS and be told to actually administer an online version of this course. If I was a new student and thought this class was representative of OMSCS as whole I would be assuming this degree is a joke and dropping out of the program completely.

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

  • j+rann0g9HeMxS4wpX+LXw==2023-12-29T20:10:52Zfall 2023

    Introduction to Analytics Modeling

    Pros: subject matter was interesting, lectures were clear, TAs

    The topics covered were the best part of the course. The material is interesting and highly applicable. Dr. Sokol states at the beginning that he is essentially toeing the line of giving enough information about a lot of topics without going into a complexity that goes beyond an intro course. I believe he accomplished this goal well in his lectures, as they were well explained but not too complicated. The TAs were helpful, highly responsive, and receptive to dumb or rude questions/comments.

    Cons: not many opportunities for application, tricky assessments, course structure

    I most of all wish there were more example (independent work) problems in the course for students to practice. There are check-in questions at the end of each module and a coding homework assignment each week. While the homeworks were helpful in helping me develop understanding of the concepts and the basics of how to implement them in code, they just weren't enough for me personally for the assessments. The course is structured moreso as a notetaking course in which you take the knowledge from the lectures and apply them straight to the test with little practice beforehand. For instance, many of the questions on the assessments ask you to apply topics in the lectures in ways that I had never practiced independently before (e.g. the modeling decisions in midterm 1 and the simulation questions on midterm 2 stand out to me). I don't see why there aren't at least some supplemental resources to help students practice more before the assessments. I often felt as if I was being assessed on several topics for the first time on the assessments, which contrasts heavily with 6040, in which there was a multitude of practice options.

    The assessments have some tricky wording, but they were otherwise pretty fair. Outside of exam week, the work can essentially be done quickly - watching the lectures and then a few hours on homework (more in the beginning if you don't know R). I studied for about 5-10 hours on each assessment and averaged ~89 on all three.

    Overall, though, the course isn't too difficult, so I ended with a ~90.9, which was considered an A. In some ways, though, I don't feel as if I actually deserved the A because I don't feel overall confident in my ability to apply the topics beyond a cursory level. I think the course can be improved just by adding in some more opportunities for students to practice applying their knowledge.

    Rating: 4 / 5Difficulty: 3 / 5Workload: 10 hours / week

  • M6dSuPXA8TJGXrhuAJU1SQ==2023-12-29T00:02:05Zfall 2023

    Introduction to Analytics Modeling

    Pros: The course is a good one to introduce you to the analytics, it covers a lot of aspect of the analytics. The homework is not too hard and the schedule at the end of the semester is better than previous.

    Cons: Need to do homework almost every week, plus the peer review, I feel that I have no time to take a breath. The percentage of the 3 exams weight too much higher (75%), you will easily drop from A to B if you did bad in one of them. I did bad in the second midterm exam but luckily I got 87.75% overall and still consider A. Overall it's a good course without too deep dive in one of the area but covers broad area.

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

  • xs7j2pMhubclKnZeSlxzrA==2023-12-28T02:46:37Zfall 2023

    High-Performance Computer Architecture

    Overall a great class. Some of the best lectures I’ve seen so far in the program. You will get a solid understanding of how processors work, instruction scheduling, memory, and caches from a functional and a performance lens.

    Exams were fair and actually tested application of class concepts as opposed to memorization (thought some of that was still present)

    Assignments were interesting but different from most I’ve taken in the program so far. You run certain programs on a simulated processor and gather statistics from them. Those are then used to fill a word document. The later assignments require you to change the processor code base to gather statistics needed to answer the questions. The code base is hard to understand and it is hard to get to the exact numbers that some of your peers might be getting.

    Overall would recommend

    Rating: 4 / 5Difficulty: 3 / 5Workload: 14 hours / week

  • 1CORjIMb+/U0HHRkTcH+4w==2023-12-27T07:53:29Zfall 2023

    Special Topics: Quantum Computing

    This was my seventh course in OMSCS. I have a CS undergrad degree and 4+ YoE in software development. I got an A in the course.

    The course gives a great introduction to the world of Quantum Computing. It starts from the basics and is not very Math or Physics heavy. It focuses more on the computing aspect of Quantum and doesn't need any prior knowledge of Quantum Mechanics. It gradually builds on the basic concepts and moves on to more advanced concepts in the field. The material covered is a mix of basic concepts from the textbooks and concepts from interesting recent research papers in the field. The course does require prerequisite knowledge of basic linear algebra and matrix/vector operations (but it can be picked up while in the course too).

    The lecture material of the course is very well done and Prof. Moin has kept it short and crisp with ample resources to explore more if needed. The course lectures compliment the textbook for first half of the course. It starts with the basics of quantum computing and introduces the concepts of superposition and entanglement. It explores quantum gates and circuits and builds towards simple and advanced quantum algorithms to solve problems. The second half of the lectures dive into the more advanced and recent advancements in near term and fault tolerant quantum machines and are based on research papers. Quantum errors and benchmarking, NISQ computation, error mitigation and error correction techniques are explored.

    There are four programming labs in the course which uses IBM’s Qiskit toolkit to build and execute quantum gates and circuits. The labs are a lot of fun and you get to implement many of the concepts/algorithms learnt in the lectures and execute them on simulation or on real IBM quantum hardware. All the labs are very closely related to the lecture material covered and they are fairly easy and enjoyable. There is a lot of documentation and tutorial around Qiskit which makes it easier too.

    There are four problem sets which test your conceptual/mathematical understanding of the material. Fall 2023 was the first semester Problem Sets were introduced. They were fairly simple and a good way to test your understanding of the material. You’ll need to go beyond the lectures and read the textbook to solve the problems in the first few problem sets.

    There are five paper reviews in the second half of the course. These are the five research papers which the second half of the lectures are based on. All the five papers are recent and interesting reads – which talk about a novel approach to solving some problem. There are weekly knowledge quizzes which keep you on track and reinforce the material learnt.

    Finally, there are two exams. The final exam is cumulative. Both the exams are closed book and closed internet but is not proctored. You are allowed to bring one sheet of handwritten notes with you. The exams test you on the concepts and techniques and are not memorization based. There will be a lot of numerical answer questions which require calculations. If you’ve understood the concepts, then the exams are fairly easy and you are given a fair amount of time to complete it.

    The TAs of the class good. Ruixi Wang was the one TA who was pretty much running the class single-handedly. He did an excellent job. He was active on Ed and Slack and also held recorded tutorial sessions for few labs and problem sets (for the first time in our semester). All TAs held weekly OH but I didn’t attend any of them. Prof. Moin did hold an OH every week but it wasn’t recorded and I couldn’t attend any of them. But it would have been nice if he did come on Ed too and answer few questions.

    Overall, I think this is a fantastic course and a great introduction to this new and evolving field of Computer Science. The workload is also on the lighter side and the grading is lenient, making it an easy course and a good one to pair in Fall/Spring semesters. I would strongly recommend this course to everyone interested and curios about quantum computing.

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

  • 7BF2kgYR9AixoKkfmxOLUA==2023-12-24T04:33:02Zfall 2023

    Graduate Introduction to Operating Systems

    Background: Non-CS. First course.

    This course is life changing. It made me someone from only being able to solve algorithm problems or fix easy software bugs to feeling confident in saying "yes, I code, at least sometimes." If you have a better background than me then it should be doable with less time.

    Because of the slack community, it gets even better. This is where you make friends and discuss future course or even career plans. I would still take it as a first course even though it's a little too tough sometimes.

    Don't worry about the documentation or exam. There's a generous community (if you care about learning and the projects) and a generous curve (if you care about the grade) to make up for some shortcomings people complain about, and the flaws resemble what you will need to deal with in real life more so it's even better.

    Project2 is the best. Don't miss it.

    Grade: 90% (A) 100% on autograder, lost some points from readme. 8X% on exams

    Rating: 5 / 5Difficulty: 5 / 5Workload: 50 hours / week

  • B9ODZY/BT5HrZA2E/KF9WQ==2023-12-24T04:24:31Zspring 2023

    Internet and Public Policy

    I am in the OMSCS program and wanted to take a lighter semester. Honestly, this class was very interesting and enjoyable. There are 3 group projects and the last project is an essay on a cyber attack. All 4 projects are pretty much straight forward "read this, research a bit, and write about it" with the exception of the first project (phishing campaign), which is not too difficult if you are a software engineer (and it's honestly fun).

    I would take walks and listen to the lectures, which are sometimes long and dry, but for the most part interesting. Overall, I enjoyed this course and thought it was straight-forward and well-defined.

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

  • my0Z508WC2x34SdYyc4ixw==2023-12-23T18:55:40Zfall 2023

    Graduate Introduction to Operating Systems

    This was the first course I took in OMSCS. I do recommend it as a first course because it is challenging enough to force you to re-learn good practices for keeping up with coursework.

    I work in SW development, primarily in C and Java. I have only done a bare minimum amount of C++ programming back in my undergraduate days. I took this course as a refresher and to help me re-learn how to be a student. I worked full time while taking this course and I spent about a month on business trips. My biggest challenge was time management. It wouldn't have been bad if I was not traveling so much. I didn't sweat the actual material because it was largely (not completely) review.

    Projects: My biggest and only complaint is that the project requirements and specifications were distributed and vague. The git hub pages posted most of the information and a general sense of required end behavior, but some of the specific behavior could only be confirmed through documentation placed inline with with code and/or piazza to clarify gaps in the specification. I had to use gradescope with every project to make educated guesses on what behavior they were truly looking for based on the test cases I was failing. This - guessing my way around vague requirements - ate up more time and brainpower than actually implementing the code.

    That being said, the projects were non-trivial and pretty fun to work on. There was enough room for creative freedom to make unique choices in the project in terms of how to implement certain behaviors, backing data structures, and overall data flow.

    Do you need to know C: Yes. It will make your life easier. Projects eat up your time due to the complaint mentioned above. Spend your time and brainpower fighting with the vague project documentation and not learning how to code. C is not a language you can improvise your way through. They have memory sanitizer and Valgrind checking your deliverables for memory leaks. The code has to work and it has to be well written.

    Do you need to know C++: Not really. I barely remembered anything about C++ from undergrad, but I know OOP very well and I know C very well. I was able to BS my way through C++ coding with that knowledge, and you can too. It does make the last project take a little longer to do, but I don't think it's worth learning C++ over.

    Project 1: Had three different sections total, I spent 1 weekend per section. 1 day to code, 1 day to test. (~ 20 Hrs. Total) Project 2: Part 1 ~6 hours; Part 2 ~20 Hrs. Project 3: Part 1 ~ 8 hours; Part 2 ~30 Hrs.

    We had 4-5 weeks to do each project, so 20-40 hours over that many weeks is not bad.

    Coursework: The videos are a little bland. Having some learning questions in the middle of a module helps a lot to break up the monotony and I wish there were a few more. However, the material is always organized well and the videos are very easy to understand. Ada is great. I recommend watching a few videos from a module at a time. Avoid watching them all in one day or letting them pile up for multiple weeks. One week in the semester there were two modules for the week instead of one. That was not fun. Don't fall behind on these if you can avoid it. It is not fun to catch back up.

    The readings are covered mostly by the lectures, but if you need to do well on exams because your project scores aren't perfect, reading those will help you squeeze some points out. If you have perfect project scores, you can get away with not reading 70% of them, and skimming the rest. They have favorite papers they like to ask questions about while the others don't get so much attention.

    Exams: These sucked - they are very challenging. There are no short/long answer format questions so you either know the material or you don't. It is possible to get a good grade, but you need to do all of the readings and pay close attention to the lectures. I had excellent project scores so I wasn't panicking over a couple of bad exam grades. Exam 1: 75 (curved), Exam 2: 65 (not curved).

    Final thoughts: If you kill it on the projects, this course isn't difficult. The curve at the end is generous. I got an A for a good amount of effort on projects, a minimal amount of effort on tests, and watching lecture videos seriously.

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

  • 2vspUlNGwSNfH+7r5GSl5w==2023-12-22T15:04:07Zfall 2023

    Introduction to Graduate Algorithms

    Mostly just memorization

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

  • mkqAekfAOfQACT7w4wXKfg==2023-12-22T13:05:01Zfall 2023

    Machine Learning

    You are required to navigate a course with:

    1. Long-winded lectures
    2. Assignments with no rubric whatsoever
    3. A docking off of 5 points if you're unsuccessful in a regrade
    4. Ambiguous feedback from the TAs
    5. Have more detail in OH, and not the assignments, which is difficult for you to watch in your time zone. Result: A grade of C, which combined with the infamous CV, has put me into probation.

    Rating: 1 / 5Difficulty: 3 / 5Workload: 7 hours / week

  • qQnr2o7EWI8hgLfri1OIRA==2023-12-21T06:16:17Zfall 2023

    Artificial Intelligence Techniques for Robotics

    This was my first course in the program, I feel it was a great start to the program in terms of difficulty. The lectures were a bit outdated and I found Chris's tutorials to be more helpful. The projects were interesting and well put together, although most of them did require manual tuning to get full grades. You do get unlimited submission on the projects and the problem sets which is really helpful. I recommend starting the projects at least 2 weeks before they're due so that you have enough time to ask questions/tuning and not stress yourself last minute. The midterm and exam were okay, if you do well in the projects you don't need to worry too much about them as they're only worth 20% in total and you get 2 attempts. I studied for them 2 days in advance and was able to do well in them.

    Rating: 5 / 5Difficulty: 2 / 5Workload: 11 hours / week

  • FTDvYUzuGlRv6W1P25P2Hw==2023-12-21T00:50:09Zfall 2023

    Special Topics: Financial Modeling

    Easiest course I have taken at all of OMSCS. I got 99.55% (A)

    The material is interesting.

    Can pair with any other course.

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

  • uqYiQQ6tFYhzOpVV5LEVrw==2023-12-20T17:44:47Zfall 2023

    Machine Learning

    First thing to note - office hours are required. I did poorly on the first few assignments because I didn’t know this. The assignment instructions do not detail everything they are looking for. They give way more information on the office hours which you wouldn’t know otherwise.

    The assignments are quite a bit of work. Even if you have a strong programming and machine learning background, make sure to budget a lot of time for these. I was able to do some assignments in 3 days but it was quite the scramble. If I didn’t have other commitments I would have been trying to spend at least 25 hours over the course of 3 weeks per assignment.

    The exams were fair. I thought some of the questions were very ambiguous and depending on context that wasn’t given but the course videos give you everything you need.

    I thought the grading was really harsh and we were often waiting many weeks for our grades back. This is why it took me so long to realize I was missing details from office hours.

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

  • k/nTqbO1bkT82pz6K7v1GQ==2023-12-20T16:42:07Zfall 2023

    Machine Learning

    This is the first course in the OMSCS, and my work is not related to ML, which is quite challenging for me.

    Fortunately, I got an 'A' with 76%. The lecture videos are very old and provide just a basic idea of each concept. The four assignments drove me crazy because they don't provide clear requirements. However, the TA FAQ posted in the ED discussion provided some hints. The exams are all multiple-choice questions, which are based on basic concepts of ML.

    My suggestion is not to take this course in the first term. It could diminish your interest in ML.

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

  • nuRLt7ornR308OqoiaEAnQ==2023-12-20T14:58:45Zfall 2023

    Machine Learning for Trading

    The focus is on stuff unrelated to learning.

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

  • OTMORSBgU2cPEuwKJq+EwA==2023-12-20T14:29:35Zfall 2023

    Special Topics: Introduction to Computer Law

    This class is easy. Really, really easy. I cannot see how a native English speaker could get less than a B in this class assuming they didn't skip assignments. Assignments: weekly lecture quizzes (the lectures open up week by week), weekly discussion board posts (need 10/12 for full credit), 2 major writing assignments on corporate acquisition and code analysis for infringement. Generous extra credit on discussion board and code analysis project. I wanted to pair GA with something to maintain momentum and this was that class. It honestly was pretty interesting and now I know more than I did before about intellectual property so I don't regret it.

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

  • 7tMIguGI/134VxaJ7z3aPQ==2023-12-20T03:46:10Zfall 2023

    High-Performance Computer Architecture

    I've have a lot of good things to say about this course. Apart from the fact that Nolan is the best TA, I also enjoyed the lectures and content. This was my first course where i got to understand how instructions are scheduled and executed at the processor level. It was both new and enriching for me. The professor is punctual with grading exams, and if you attend the office hours he is very eager to explain the concepts to you, you can even ask beyond what's covered in lectures.

    Pros:

    • Nolan
    • Assignments are pretty interesting and supplements the material
    • Lectures are very clear
    • Grading is fair

    Cons:

    • Too much content. You may find yourself short on time to cover everything for the final exams.
    • You need to score 90+ to get an A. This leaves a thin margin of error for exams and projects.

    There are no books or research papers required for this course. Lectures suffice. For projects, make sure to follow the instructions/FAQs given by Nolan. Most times you may feel that completing projects is almost like following the given instructions to the T, but given the time constraints i feel it is fine. Anything more, and you will be struggling to manage other things.

    Rating: 5 / 5Difficulty: 4 / 5Workload: 80 hours / week

  • 6v6NWG6Kl/hPv2eJJuS8gA==2023-12-20T03:07:33Zfall 2023

    Machine Learning

    ML is the best and hardest class that I've ever taken. If you come prepared, then this is an opportunity to learn a ton about machine learning and build up confidence as a practitioner in the field. You can do some awesome projects and dive into super interesting stuff if you are so inclined.

    This is not a hard class in the way that a difficult math or algorithms class is. ML is hard because you have to quickly pick up new concepts, conduct experiments, and explain why you did what you did in a way that relates theory to practice. In short, you have to learn how to say something intelligent about things that you hadn't even heard of a few weeks previously.

    The biggest challenge is that, with each project, you have to bring a bunch of different stuff together, but it's not even clear at first what the difficult problems with any of this will be, or how you might intelligently structure your process. So you have to start by making a mess. Try out different data sets, algorithms, metrics, and ways of plotting your results. Get your hands dirty. But at some point that mess needs to get cleaned up and coalesce into a reasonable set of experiments and a paper that actually has something to say.

    You really want to come in to this class with decent programming and math skills(especially probability), and some prior knowledge of machine learning. ML4T, RAIT/AI4R, and Georgia Tech's undergrad probability and linear algebra moocs on edx helped me to prepare.

    Do not take this as your first course at OMS. The workload is brutal and you want some successes under your belt before taking it on. I really wish I would have followed the advice to watch the first 6 weeks of lectures before the semester started as doing that can buy you time to rest later in the semester.

    For reference, my scores were A1: 100, A2: 100, A3: 72, A4: 90, midterm exam: 94.25, final exam 89.3, final grade A.

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

  • CeCFXKcZ3Al9kC8Wvk5heA==2023-12-19T19:40:59Zfall 2023

    Mobile and Ubiquitous Computing

    OK. Class basics: So this class is a guaranteed B and easy A. I got an A, and based on the rubric grading, I cannot see how someone could less than a B. A lot of modules broken down into about a dozen topic areas. "Prompts" are just unlimited attempts roughly a week apart (maybe a little more sparse). Then there are one or two exercises in each topic area. The exercises take a little more time, but honestly, I do not think more than an hour (most extremely less time). There are two group exercises that do take a bit more time. You get put into a group for the first one. Then you have a group project where you can pick your team. This is the bulk of the grade. Again, I got a really great group. I did socialize early though. Yes you have to put time into this, but again nothing really that big. I said I put in 6 hrs/wk (at least half of the course though was much less). I think that is accurate. That time was really due to the group project more than anything else. This semester you had to write a couple pages for research instead of a midterm. This was the only thing I got worried about due to work life balance. But I knocked it out in somewhere between 3-4 hrs. Again, the grading is abouth the rubric and the grading is generous (although I may have disagreed on some points, who cares in the end if the grade is still an A ). Now for the course itself. This is basically just a survey course of the material that is out there. I didn't learn a whole lot that could actually "be used". And I am not referring to learning coding algorithms, just having anything coming from the class to use. Just a couple peices of useful information every now and then (and that wasn't much). This might be appealing to people though. The instructor always has his Google Glass glasses on because he worked on them. It gets really obnoxious. Also the product is always being brought up as if it is the pinning achievment of technology. It just got annoying to me after a while. After taking the class I try to think what I learned, and I really can't say I learned anything. So easy A, but fairly useless class.

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

  • bOPQOXqs5ggIj1uXZmTCZg==2023-12-19T16:41:19Zsummer 2023

    Information Security Lab: Binary Exploitation

    OMSCS candidate, under grad in CS, played CTF in college. Not bad if you’re already familiar with basic binary exploration, only need to solve 5 challenges a week to get an A, I did them all on Sundays.

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

  • w8O28GZbi4QsOKRokvPQ3w==2023-12-19T14:03:27Zfall 2023

    Machine Learning

    This is a follow-up review to a review I posted below. ("I'm on the cusp..")

    I managed to get an A in the course with an overall grade of approximately 83 percent. I was saved at the end of the course by doing above the median on the final and getting a 100 on the last assignment.

    Now that I've had some time to reflect, I've got two major takeaways to add to my comments below.

    1. The ambiguity in the course stems from two words: "interesting" and "why".

    Interesting: In the project specs, they ask you to pick an interesting dataset. Interesting in this case means interesting in terms of its interactions with the algorithms, not the content of the dataset itself. If you have a simple dataset that can iterate quickly over the algorithms and tease out the behavior described in the lecture and the textbook, and keeping track of pros and cons of different hyperparameters, you're on the right track. A large dataset that gives murky results, even though it may be really really cool, isn't what they're looking for. Diabetes, Cancer, Shark Attacks, and Forest Fires are your friends.

    Why: When you're answering the questions posed in the assignment, make sure you describe how these results relate to the content of the data, the algorithm, and the math behind the algorithm. If you can come up with a coherent explanation that ties the three together, you're on the right track. This is the biggest stumbling block that they penalize you for - once I understood the level of detail they needed, it was simple (though not easy) to give them what they wanted.

    1. The tests are definitional with a hint of applicability. If you're able to address the "interesting" and "why" in 1), you're in pretty good shape, but you're going to have to do a bit of memorization, as well as reading carefully to ensure that you've understood the question. Also, the exams are multiple answer, and you are penalized harshly for selecting a wrong choice - I lost about 10 points on the first exam by guessing. Err on the side of selecting only the things you know are correct.

    I got an A allocating my time to the projects and studying. I read the office hours and FAQs on Ed, but never attended or watched office hours (they wouldn't let us download them, and I tend to watch when I'm offline).

    I spent the majority of my time on the papers figuring out the behavior of the algorithms and coming up with compelling graphs. I spent a locked-in day writing the papers, but I wouldn't recommend this if you don't have experience writing a 10 page essay in less than 10 hours.


    Like the poster from UW mentioned below, once you understand what they want, the ask is pretty simple. It will still take a lot of work to answer properly, but you'll know what you need to do and that's the hardest part.

    If you're in the class next semester, look for, or ask the head TA, about his trinity of relationships, and focus on their definitions of "why" and "interesting". If you can get those answered, you'll be in better shape than 80 percent of the class.

    I still stand by my statement that they need smaller practice essays to bring everyone up to speed.

    Rating: 2 / 5Difficulty: 4 / 5Workload: 30 hours / week

  • jiSBPxsSUJFAZWj0X6rCyQ==2023-12-19T10:29:29Zfall 2023

    Network Science: Methods and Applications

    Course Description:

    • Lectures: I personally like text-based lectures as it saves a lot of time. What I did not like was that these lectures use notations and terms without ever defining them or providing any reference. You need to refer to the book to understand some parts. Content is Maths heavy but you do not need to understand it completely to grasp the concepts.

    • Quiz: Quizzes were mostly fair but I agree that quite a few had ambiguous questions. You have unlimited time which is sometimes useful and sometimes tricky.

    • Projects: Projects were fairly easy due to the networkx library. The only part we had to implement ourselves was the motif finding algo from a paper, which is not too difficult.

    • Issues: I liked the content, but the course never took off. It always felt like the first week of the course. Since there is no exam, it is easy to speedrun through the weeks lectures and complete the quiz. There was no effort to connect different parts of the course or enable retention of core concepts. The professor is completely absent from the course.

    Rating: 4 / 5Difficulty: 3 / 5Workload: 9 hours / week

  • jiSBPxsSUJFAZWj0X6rCyQ==2023-12-19T10:17:54Zsummer 2023

    Knowledge-Based AI

    This is not a Joyner class. This class was recorded when he was still a PhD student. KBAI lectures easily violate every single thing taught in HCI.

    Cons

    • Boring, repetitive, pointless lectures.
    • Poor assignments and projects.
    • You can hardcode through the assignments and RPM and call it 'Learning by Recording Cases'

    Pros

    • It is easy to get an A with minimal effort.
    • Coding: Using Pillow or OpenCV (for image manipulation). Implementing BFS/DFS/A* algorithms.

    Take this course if you are looking for low-effort easy A.

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

  • Kn8gG+am8vXjiWYsxuXP1Q==2023-12-19T07:50:41Zfall 2023

    Introduction to Theory and Practice of Bayesian Statistics

    Grade: 95.15%

    Theory: the material in the course is extremely interesting. Unfortunately, you'll have to do a large fraction of your learning outside of the provided lectures. The lectures are not presented well: frequently, "the what" is presented with little emphasis on "the why" behind the material. If you internalize the need for independent, self-study early on, then you should be okay. There are many helpful YouTube channels on Bayesian Statistics.

    Practice: I chose to do all code for the course in Python and PyMC. Aaron, the TA is very helpful with PyMC. That said, it would be quite nice to see the course update to exclusively using Python.

    All considered, I recommend this course, not for its instruction, but for knowledge it provides.

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

  • HEgP3uohxFl0KsOLnijblg==2023-12-19T06:14:54Zfall 2023

    Video Game Design and Programming

    This is an excellent class. I would recommend it with no doubt. The course is pretty much team-project based, with some tutorial exercises in the beginning. Therefore the product is open-ended and you can really make some good projects if you opt to. If you don't, there are also clear guidelines to make sure you get all the points. Prof Jeff Wilson is enthusiastic and the lectures are really fun to watch. He goes into allot of trivial about games and gamming culture in general. While they are not directly related to the assignments or your projects, they interesting and engaging. The TAs are usually helpful and encouraging. One thing to note is that I feel like you do need to find a good team, since you will be hanging out with them for most of the semester and making the team-project together. Aligned expectation and concepts will make your semester a blast!

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

  • HEgP3uohxFl0KsOLnijblg==2023-12-19T05:38:53Zfall 2023

    Human-Computer Interaction

    The course content is engaging and enlightening. I highly recommend everyone to explore the materials. The lectures are not only interesting but also of high quality. Depending on how well you want to do, the course may take up any where from moderate to a significant amount of time.

    However, the majority of your time will be spent on completing essay assignments, which was a terrible experience. Assignments are graded by human graders who follow a rubric. However, some graders do not read the submissions. Instead they provide random, sloppy, and unjust reasons to take points off, probably to pretend they are actually doing their job by giving varying scores.

    I had gotten absurd reasons for deduction like,

    1. Answer dose not count due to not starting the sentence with “This is because...”.
    2. Paragraph dose not count because it "feels like the wrong font", despite the font is exactly the one provided. This grader probably thinks he/she is a font-Jedi who can "feel" fonts.

    While there is a re-grading process, some regraders also does not know how to read. I've had a regrader mistakenly review a completely different questions, and watch him twist words to justify how the previous grader's irrelevant comments are applicable here. The ridiculousness and amateurishness of some graders would have been laughable, if not for the fact that they literally hold power over the grades on your transcript. All in all, the assignment grading process encourages you to dumb-down your writings and make it as superficial as possible. Therefore they are inconducive to learning at best, and a time waster at worse.

    Such deficiency a true bummer, since Dr Joyner is a excellent educator and force for good. The lecture and reading materials are really eye-opening.

    But due to the experience with assignments. I would not recommend taking this course for academic credit. I do however highly recommend going through the course material in its entirety, either on your own or with moocs, this would provide an equally enriching or even better experience

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