Reviews

Pros: 1. Primarily a hands-on course with most hours spent on projects 2. HWs are pretty straight forward up until HW3 and then go off the rails 3. Extra credit on sequence modeling was challenging, but tied in with lecture material Cons: 1. Waaaaay too much time expected for the homework projects (and not distributed well) 2. Too many lecture materials; too broad (lectures are pointless after HW3 anyway and don't cover what is needed) 3. TA sessions are useless, especially with only 30 mins (use Piazza instead) 4. Poorly designed overall Detailed Review: I had read previous reviews here and was excited about taking this course. I was thinking because of all of the project work I'd come out with some deep, practical knowledge. Unfortunately, if someone told me to build a network to do X I doubt I would be able to. The lectures start out pretty good and match with HW1 and HW2. Then something happens - HW3 gets incredibly hard and the lectures start diverging and never come back to what you are actually doing in the homeworks. After HW3 there are a lot of assumptions about what somewhat knows and the lectures don't cover what is needed. The lecture materials continue on this broad track and are so high-level that I doubt I'll remember anything. Our group started on the final project 5 weeks out and I personally spent a total of 36.25 hours. Near the end homeworks start piling up and work starts to overlap even if the due dates don't, so it becomes a bit of a mess to manage. Prof. K. actually seems like a cool dude. His lecture style is decent and he has put a lot of work into creating the projects, auto-grading system, PySuperTuxKart (PyStk). He even pops on Piazza from time-to-time, which was much better than the non-existent instructors for ML that I took alongside this class. Unfortunately, this course is just so poorly designed. If half of the lectures were dropped and more of the lectures were spent to tie in with the homeworks I think this could become one of the best courses. Instead of going broad, a person could walk away with deep technical knowledge. Mostly, I just felt like a monkey twiddling hyper-parameters until the project passed the local grader. I took extensive notes of the time I spent each week, so will share in case any of you are wondering how you might budget for the projects: - W1: 9.5 hours (didn't start HW1 yet) - W2: 9.5 hours (completed HW1) - W3: 4.5 hours (didn't start HW2 yet) - W4: 12.5 hours (5.5 hours for HW2, 5 hours for lectures, 2 hours for start on HW3) - W5: 5.5 hours for HW3 (still in progress) - W6: 18 hours for HW3 (completed HW3) - W7: 38 hours (16.75 hours for HW4 (completed); 3.75 hours for HW4 EC; 12.25 hours for HW5/lectures; 3 hours for Final Project (FP); 2.25 for EC HW) - W8: 20 hours (13 hours for EC; 1.5 hours for FP; 3 hours for HW5 (completed); 2.5 hours for lectures) - W9: 18.25 hours (8.25 hours for FP; 0.25 hours for OH; 9.75 hours for lectures) - W10: 14.5 hours (FP) - W11: 9 hours (FP)

Pros: 1. Little to no course material required (readings, etc). 2. If you have a base in ML already, this course is generally pretty easy. Cons: 1. Quizzes are annoyingly nitpicky, focusing on wording and details instead of bigger picture concepts and practicality. 2. Quiz wording is often ambiguous and assume knowledge that's not in the lectures or readings. 3. Single submission quizzes focus on spending time on the quiz rather than learning. Detailed Review: If I could undo taking this class I would. I don't I've experience a worse course in my entire academic career. I feel like I paid for a dubious Coursera or Udemy course instead of course that representative of UT Austin's prestige. Other courses in the program have been extremely useful to my growth and understanding of machine learning, but I cannot say the same about this course. This course consists of quizzes over very dull lectures, followed by a final project that is free-form (similar to a research paper). The lectures are basically copy pasted content from Coursera the book "Hands On Machine Learning with SciKit-Learn and Tensorflow". If you're interested in actually learning about machine learning, you're better off buying the book and self studying (which is infinitely more detailed than this class) instead of dealing with this class. The project is only interesting because you get a lot of autonomy with it, but is otherwise nothing special. If you need an easy credit to finish off your degree, this is the course for you. Otherwise, don't waste your time. Take another elective that will actually benefit your career.

Pros: 1. Early access to all assignments 2. Textbook is free and you can skip the lectures because they don't help Cons: 1. The class is a self study of the really dry textbook 2. You're on your own, especially dependent on the TAs Detailed Review: Don't take this class. It felt extremely tedious and was the only class out of the ten that I felt frustrated. It's one of the most low effort classes from a staffing point of view. As a student, it was extremely challenging. You're on your own to read the textbook and to try to complete the assignments and quizzes. If you can't figure it out from reading the textbook, then it's going to suck and you will have to spend a ton of time looking for outside resources for help. The only helpful resources were the other students in the discussion forums. The textbook was the driest and most boring thing I had to force myself to read. If you don't keep up with a lot of the concepts and notations, the chapters just get worse. There's also no example problems in the book to help you with the assignments, yet if you have any questions, you'll be directed back to the book. Skip it, it sucked, still feel like I don't know much about the subject.

Pros: 1. 2. 3. Cons: 1. 2. 3. Detailed Review: Pros: 1. Project not too hard Cons: 1. test cases are hidden, be sure to check if your memory is leaked 2. there are hidden test cases 3. you only know your grade after the project deadline, no gradescope

Pros: 1. No exams 2. Not bad to take during summer 3. Cons: 1. No real world application whatsoever 2. The worst sample code you could possibly imagine 3. Detailed Review: I can't remember a class that I took that I took less away from. I can confidently say even after getting an A in this course, I don't know any more about regression now than I knew going into this course. There are no practical problems assigned. There are no practical applications discussed in lectures. I gave up even watching them after the first week, because they are just reading directly off of the slides most of the time. It is very disappointing that this is considered a "foundational" course. I took absolutely nothing away from it. The sample code provided is a total joke. Just really disappointing all around.

Pros: 1. Easy course if you are a TA, because you can be the biggest disappointment ever and still get away with it. 2. Easy course if you are a professor, because you need to spend 0 effort or care. 3. I'm trying hard but can't come up with a 3rd Pro. Cons: 1. Horrible logistics 2. Useless TAs (and professors), no guidance whatsoever 3. Dull content, nothing exciting (they make it unexciting, I should say) 4. Weak, useless assignments and extremely poor solution sets 5. Contact me personally and I'd be happy to spell out 100 more cons. Detailed Review: I felt it was one of the most horrible courses, if not the most. Previously, RL took that position and I didn't expect anything to top it; this program continues to surprise me! Professors and TAs both were almost non-existent. TAs were a complete disappointment, and professors didn't care a single bit. - Course logistics: What even was that, if any? - Course content: The first half was fine, but the prof makes zero effort to make anything interesting. Second half was so dull I kept beating a dead horse trying to find any little spark. - Edstem: There were only questions, no answers. For example, 4 days into the final exam window, we would still not know what the exam structure is (we never knew, if you didn't see that already). The TA (I want to spell out the name but I'll let go) responds after a lifetime saying "refer to practice questions" and unsurprisingly, no practice questions ever existed. Apparently a few weeks into the course, the consensus was already established that nobody cares about this class, it just exists. - Exams: I liked and enjoyed the quality of exams, they make you think (sometimes out of the box) and actually apply the concepts which you never do (or should I say - they never let you do) in the entire course. Unfortunately, I see many students struggle here and it makes complete sense because you don't get exposed to anything of good quality throughout the semester except the problems all of a sudden in the exam. - Final thoughts: I regret taking it but if any consolation (if at all), it cleared one theory requirement. - Overall: Stay away, take other theory class(es). I would give this class a negative rating if it were possible. Update: There was utter confusion regarding grading with many students left hanging and frustrated. As usual, there were only questions with no responses on Ed. One fine day the professor woke up from a semester-long nap and posted on Ed about the grading. And IMMEDIATELY, the course was archived which means no one can ask any potential queries. They messed up the grading of at least one student as I know of, and I'm sure it has been a nightmare for him/her to deal with this. On another note, they apparently made the grading stricter this time, with no curve. Though I personally wasn't affected by this part as I did exams very well, many students had a rough mid term so it possibly affected their grade.

Pros: 1. Very easy course to score highly in 2. The HW problems that involved data analysis were interesting. 3. Cons: 1. The lectures are terrible. (Can I list this for Cons 1, 2 and 3?) 2. 3. Detailed Review: The course is definitely the worst of the 4 classes that I have taken so far in the program. The lectures are highly theoretical and it seems like the instructor is just rambling and reading directly off of the slides. The course grade is based only on homework, so it is easy to get a high score in the class with little effort. The HWs definitely varied in difficulty and utility. Some were highly theoretical (I didn't get much out of these), while others had data analysis portions that were useful. I would definitely recommend taking Probability before this course as it builds on some of the concepts from that class.

Pros: 1. Great if you want to learn open source PintOS crap that is already freely available 2. Great if you want to do projects that are completely disjoint from the lectures. 3. The material got an upgrade but the lectures are still from 2019, so you will be taking two subjects for the price of one (p.s. they both suck) Cons: 1. Refer Pros Detailed Review: The assignments are copy paste from Stanford PintOs which is about 10 years old, the lectures still talk about advances in CPU architecture from 2019. The course got an updgrade by going back 5 more years. The assignments are upgraded but there is no support for it, refer the KAIST materials to get an understanding of what's going on.

Pros: 1. Material 2. 3. Cons: 1. Staff 2. 3. Detailed Review Literally, it was all about guessing what Zigler's intentions or assumptions behind questions in assignments or exams. Lectures were basically paraphrasing of the textbook and some articles. Regardless of how good and relevant the material is, this isn't a way to measure performance. It's like a matter of instructor's ego maybe rather than the understanding of students (really unclear and they were not willing to clarify the answers in some cases). In another course, we had similar situations where more than one answer made sense under different assumption, and professor happily acknowledged the confusion and considered the other approach correct. For what it matters, I'm getting A in the class; nevertheless, I wouldn't recommend this class.

Pros: 1. Extremely low time commitment other than the final project Cons: 1. If you don't have prior ML experience, you will not learn anything from these lectures. If you do have prior ML experience, you will not learn anything from these lectures. 2. Quizzes are riddled with grammatical errors, and they test your ability to match sentences from the slides word-for-word rather than test your knowledge of machine learning. 3. Gaslighty TA Detailed Review: If you're looking for a class to take with little to no time commitment, this is for you. Your grade is based entirely off six 10-question quizzes and a 10-page final project. The bad news is, the quizzes are written in broken English. Now, as a lifelong STEM student, I am very accustomed to professors and TA who do not speak English as a first language. It's never been a problem for me. The bigger issue is that the quizzes are taking things directly from the slides, and tweaking a few words to create some "wrong" answers. That means it's very critical to understand exactly what the question is asking, and understand if the wording is intentionally deceptive or an unintentional error. If the assignments were structured in a way that would really test our knowledge vs our reading ability, I don't think the language would be an issue at all. My biggest complaint is the TA. Sometimes he would be responsive, but one of the first times I raised an issue with the wording on a quiz, he told me it was my fault for misunderstanding and completely dismissed my (extremely valid) complaint. I saw someone else raise the same issue in a public piazza post rather than private and he was a lot more responsive. In addition to fear of receiving a combative answer, I also didn't find many of the Piazza answers particularly helpful, so I rarely posted. You're on your own in this class.