Reviews

Pros: 1. 2. 3. Cons: 1. The material was vague, the explanation was vague, 2. TAs were of no use, the most answer they could give during office hours: "umm, I can't say more or I will give away the answer' or 'umm, yeah' or 'let me think how can I answer without giving you too much information'. 3. Professor sent email of penalizing students while the final exam window was open! Detailed Review: The course was very vague, material was not well explained. The quizzes and the exams became 'what Zigler thinks' not what seems appropriate to majority of the class. TAs were very useless. I have taken 7 courses and I found this course had the worst TAs. Professor announced right during the final exam that he will penalize some students as they could not grade the last assignment 'as per his standards'. This is very unethical that you decided to bring down the moral of some students while they were preparing for the 35% grade for the class- the final! He should have sent that email after the final, not during it. The quizzes answers given were one liner or two liners. And sometimes he will say you have to have the exact word to get the grade just explaining will not give you full credit. The worst structed course in my experience so far.

Pros: 1. Interesting material. This course had a lot of potential Cons: 1. Lectures don't relate to labs 2. Little to no Piazza support 3. Lack of empathy and hostile responses from TAs 4. Instructors ignore direct messages Detailed Review: The professors in the videos seem like nice people...but they're long gone and don't interact with online students at all. Now the class is run by TAs who, for the most part, don't respond to questions and couldn't care less about students. At the end of one office hours, a TA just said, "well time's up, see you guys next office hours" and left when it was clear students were still struggling and needed help. I also sent direct messages on Piazza to the instructors and they never responded.

Pros: 1. Not overly difficult (except HW5) 2. Reasonable summer time commitment 3. Cons: 1. "It's not overly complicated..." - lectures are endless recitations of equations with no context 2. Professor actively refused to recommend any outside resources to learn the material 3. www[tt] <- fff - the sample code will make your eyes bleed 4. Non-standard terminology makes it difficult to cross-reference outside materials (Bernoulli regression anyone?) 5. I got an A, but I didn't learn anything 6. Only one TA for well over 100 students (no complaints on him personally, just need more) Detailed Review: I went into this class thinking there was no way it could be as terrible as the other reviews say. Unfortunately, dear reader, I have bad news for you - it is indeed as terrible as the other reviews say. I'm not going to repeat the same topics as all of the other reviewers, except to say that this *should* be an interesting class. This *should* be a relevant class. There is a lot here that is foundational to data science and machine learning. But the professor does such a poor job of communicating it that most opportunity for learning is lost. It's not just the worst-taught class in the program, it's the worst-taught class I've ever taken. Protip - don't bother with HW5. Just use one of your two drops and move on.

Probably one of the worst classes I have taken. The topics are fine but no real cohesion from one week to the next; it feels like a bunch of jumbled important topics that are connected if you look deep enough on your own but are not presented that way. I found the lectures to have so little utility that I ended up learning everything from outside sources (textbooks: CLRS, Kleinberg & Tardos; lectures: tim roughgarden (http://timroughgarden.org/w16/w16.html), Jelani Nelson (http://people.seas.harvard.edu/~cs224/fall14/index.html), and Erik Demaine (https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/ and https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/index.htm). The homework problems can be difficult to understand and going to TA office hours is practically a must. This course also had peer grading for homeworks (typically you grade 3 peers) and it was not uncommon to see scores from different peers differ by over 30% of the total assignment value. Most times the instructors took a simple average of the scores your peers gave you. Tests felt to be largely an exercise in having the right information written down on your 1 allowed notesheet, but with test questions being so different from problem set questions it can be difficult to determine what is the right information to write down. Also, some lectures would wind up on a test before they were in a problem set then show up on the problem set after the test. I would say avoid this class if you can, but with only 10 classes that's not possible; I would say save this for an otherwise easy semester. EDIT: here is the letter grade breakdown: A: 88 to 100 (20) A-: 80 to 86 (26) B+: 76 to 79 (11) B: 67 to 74 (10) B-: 61 to 64 (5) C+: 52 to 53 (2)

Pros: 1. Not that hard Cons: 1. Miserable slides 2. Miserable code 3. Miserable Detailed Review: Stunning to me that UT still has this course up in its current state. Going into this course I was excited for a theoretical deep dive with some potential applications for regression. That's not what it is. This course is heavily theoretical and poorly explained. I spent more time reading outside resources than trying to struggle through Dr. Walker's explanations and slides. His attitude online was also not that great, it almost seems like he expects everyone to already have a PhD in stats? I think the most frustrating aspect was the numerous instances where the professor simply hand waived an important part of a derivation or said something to the effect of, "well everyone knows this so I'm not going to take the time to explain this in the slightest." In the same time he could have simply explained it. Working through "example" in class was also frustrating. There was no working through it. Just vomiting of stats associated with a data set which is not provided meaning you can't actually replicate the work yourself. Also, the code is garbage. The only redeeming part of it is the grading system makes it relatively easy to do well. You won't learn much, but you will do well.

Pros: 1. Flexible deadlines pushed all the way to the end of the semester 2. Simple programming assignments Cons: 1. Professors are non-existing 2. Useless lectures 3. Final exam designed for full points or no points (avg. 59, STD 16) 4. HWs, Programming Assignments, and Final all are full points or no points 5. Book is not that straightforward Detailed Review: Do not take this course unless you really have no option. Something I learned in my undergrad from one of my professors was "If a professor says to read the book and come to lectures to discuss what we read, they really don't care about teaching only their research" which is 100% true for this course. Literally the first video the professors say to read the textbook and only watch the lectures afterward. You would think enrolling in a top 10 CS university you would learn from the professors but you are dead wrong this class was just either a cash grab or it was just used for the professor to say he is teaching and can go back to his research. I say him since one of the professors has already left UT Austin and is the only one in charge of the course. He never said a single word in the Piazza or anything the course is run by only 1 TA practically. If you are actually interested in RL please do yourself a favor and go watch DeepMinds RL course it's free and you will actually learn something from some of the best. The final is a slaughter fest and we had an issue were students would post on private about their solution past the exam time and they would get full credit regardless if they got it wrong while they were taking the exam. You can already figure this created an unfair advantage for students that didn't know about this and I say lightly as there was no post by the staff stating we could do this, so they don't even hold up to the Honor Code. Just don't take this class you are wasting your time and money.

This class was disjointed. The homework was different from the lectures and the test were different from the lectures and homework. Also the class has no application portion. I honestly left the class not really sure what I learned. It was a huge disappointment.

I'm not trying to be unkind here, but these lectures are just awful. Lectures were comprised of just super, super slow talking, and walking though poorly written and hard to read equations. I'm three weeks in and already in "I can't believe I actually paid for this" territory. They also moved the course to Canvas this semester and, frankly, they don't seem to really know how to use it. Aren't there learning technologists to help course staff with this sort of stuff (rhetorical question - there are). Canvas is just super disorganized, peer grading is super disorganized (I have to rate this person on their feedback, but can't see what grade they gave me? What?) If this is the part of the course that folks think is well done, I cringe thinking about what the second half will be like. They've also set this up so that you can't access any of the material for the next week's assignment (including lectures) until Thursday of the current week, and all the assignments for the next week are due the following Friday at midnight. So you basically have one weekend to watch all of the lectures and complete the work per module. Not very accommodating to folks who work full time, but I suspect that may be the point. Would be remiss if I didn't point out that both the lectures and the slides (and in one instance, the sample code) are absolutely riddled with errors, with scant attention seemingly given to correcting any of this in a really meaningful way (some corrections scrawled underneath some of the lecture videos in a few instances, but that's about it). The assignments for the first half of the course really just feel like pointless busywork. I also have to call BS on the other reviews here. Not sure which course they're talking about, but it isn't the one we're in this semester. Super disappointing overall. If this is the future of online education, count me out. ______________________________________________________________________________________________________________________________ UPDATE: Heading into week 5 of what is effectively a 10 week course (2 / 12 modules are "optional") and we've received, wait for it...grades for only the first assignment. That means that we'll effectively have completed 80% of the deliverables in the first half of the course while only having gotten feedback on a single deliverable (or 20% of the deliverables for the first half of the course). Not too great, if you ask me. Also, they forgot to release the week 5 material until prompted by students and there was basically radio silence (i.e., not a single question answered by course staff) in edStem for maybe 7 - 10 days (again, until a student piped up and complained about this). Instructor response was basically a polished version of "answer your own questions". Really, here it is verbatim: "I have been reviewing the activity on Ed Discussion this week and have generally been pleased with students helping each other interpret homework questions. In particular, figuring out what is meant by a bounding box means really understanding what makes something a point pattern. I have posted an answer now, but would like to take this opportunity to share my appreciation to the students who have responded to these posts. This is exactly the type of discussion that helps learning and how the Ed Discussion tool should be used. As for the activity of instructional staff, we are doing our best to keep up with the volume of administrative issues that have come up this semester. We monitor Ed Discussion routinely, but cannot write a response to every post." When you are actually in the course, you will understand the context for the question that prompted this response, and why "figuring out what is meant by a bounding box" *isn't* just a matter of "really understanding what makes something a point pattern". ______________________________________________________________________________________________________________________________ Week 5: They "cancelled" the staff graded HW for week 5, ostensibly to give themselves more time to grade (if only there were some way to predict - in a course on advanced predictive models - how much effort would be required to manually grade these assignments). Instead, we have some clearly very hastily thrown together peer graded homework which amounts to a bunch of pointless busywork. ______________________________________________________________________________________________________________________________ UPDATE 2: Not sure what's going on with the prior reviewers, but I am personally finding the second half of the course much better put together than the first half. Yes, it's true - there are no coding walkthroughs. But it's Python...Google it (or already know it). Lectures are much better, instructor is much, much more engaging. Why we couldn't have Professor Sarkar teach the entire course, I have no idea. I have little doubt that this would be a much less demoralizing experience if she did.

The book is your savior and is absolutely hilarious, ridden with sly jokes. The author and I must share a very similar sense of humor. The exams are not very fair. Review 3 said the projects are dry. This is a severe understatement, they are drier than your mother in laws chicken she unequivocaly makes every time you come over--hopefully this statement resonates with at least one person and provides he or she the comfort that they are not alone in this unspoken subjugation. The projects require a dummy amount of research.

Pros: 1. Deep learning is an interesting topic. 2. PyTorch tools are great to learn and relate to NumPy closely. 3. Final is a group project. Cons: 1. Workload (390 hours over the summer is 48hrs/wk). 2. Lectures have no relationship to the assignments after the first few weeks. 3. 85% of the lecture material has no use in the assignments. Detailed Review: I do not recommend taking Deep Learning. It's a huge amount of work for no payoff. If you want to learn Deep Learning and also learn the toolset better, then just do all the work throughout the book "Deep Learning with PyTorch" which is written by the creators of PyTorch. This class is not useful to learn the PyTorch toolset, reading research, etc. 10-20% of the workload is building models and 80-90% is training models to achieve high accuracy.