Reviews

Pros: 1. Easy A 2. You know about most important regression techniques after this 3. Cons: 1. Terrible teaching 2. Terrible slides 3. Why am I paying for this? Detailed Review: This class is garbage. Let's start with the material. The material goes over most regression techniques you should care about. This is good. However, how everything is taught is awful. Prof basically reads off the slides. I stopped watching lectures in week 2 and just paid attention to the course materials. Therefore, it's just a self-study, right? That would be fine if the course materials weren't also complete garbage. Let's start with the quality of the slides. They are extremely disorganized, sometimes making logical jumps that require a lot of thought to understand. Then, on the other hand, the slides sometimes oversimplify everything to the point where you don't understand what is actually happening. An easy example is the week 1 slides that cover simple linear regression (without matrices). After introducing the topic there is a whole aside to sample estimates that isn't explained very well, THEN they talk about least squares (with a loss function that isn't even the MSE! I know the constant goes away in the derivative but why not include it???). The least squares explanation is done pretty well and then he mentions that you can do the same thing with MLE but doesn't really explain the specifics (what is the pdf???), just some basics about MLE that you should already know. If he's going to explain things we should know, then do that for everything instead of making crazy logical jumps at some points. Then, he says he'll go through the math but does it for a simplified case (\bar{x} = 0) that nobody would care about in real life. The not-simplified case is literally not much worse, just an extra covariance term. Why not do that? He also generally simplifies for \sigma not known which is understandable but for god's sake just give the results for \sigma not known in a slide or something instead so someone who's interested and wants to apply this in real life doesn't have to spend 20 minutes deriving it or going and googling. Then, there are so many mistakes/misleading things I've caught in the slides that are ridiculous. An example is the discussion of \hat{\sigma}^2/\sigma^2 following a chi squared. The probabilty given is not enough to figure that out since it would require a ton of assumptions to even be remotely valid. The result given is implied to be the distribution of \hat{\sigma}^2/\sigma^2 but if you actually read carefully it's not: it's the result for the RSS^2/sigma^2. Misleading. He also states the wrong thing for the variance of the residuals. Then, he doesn't mention the distribution, making you assume it's normal like most other things in that lecture but it's ACTUALLY T. It's not just me catching it: other people were posting about mistakes they found on Ed well into week 12 of the course. These examples I gave were just the most glaring from week 1. Absolutely ridiculous. Wouldn't be surprised that there is no textbook for this course for the sole purpose that more people would catch slide mistakes by reading the book. I get that this is a theory course, but they can at least mention how you should actually do this in practice. The code given is all done with matrix operations and iterative procedures. Sure, make us do it like that on the HW to learn the theory but then give us another file showing us how to do it in practice once the HW is done. I should not be having to do crazy things to run a cox proportional hazards model in real life when I can just use a survival library. And let me tell you about this man's code. It is genuinely the worst code I've ever seen. I would have more faith in an AP Comp Sci A student. It's not even about efficiency although that can be improved: it's the formatting. There are basically no comments and everything has the most garbage names that you can imagine. Variables are usually one or two letters that have no relation to what they represent. I got so fed up with this that I started rewriting all the code by myself by week 4 which was far more time-efficient than parsing his. At least it's an easy A. There are 12 HWs and the top 10 are the entire grade. No exams or quizzes or anything. Most homeworks are quite easy (exceptions I say would be HWs 5, 10, 11) and doable in an hour or two if you have any sorts of a math/stat background. Most HWs are peer graded which allows for leniency with silly mistakes as well. The TA when I took this was great and active on Ed which helped clear up any confusion that may stem from mistakes in the slides. Prof was active for the first week or two then disappeared other than questions about grades (obviously it won't be curved etc) It's an absolute scam that I am paying for this course; I feel like I was the victim of a mugging. I could literally make a better course from scratch in a week and teach it better. I thought the probability course could've been significantly better but it was at least worth a hundred bucks. I should be paid for having to take this course. If you actually want to learn regression without countless hours of deciphering slides and individual research, go to MIT OCW or YouTube: it's FREE!!!

Pros: 1. Homework, especially in the second half, is extremely easy. 2. Peer-based grading, so if your classmates are chill you'll get a 100 on every homework Cons: 1. Lectures are complete and utter garbage. 2. Peer-based grading, because the professors and TAs cannot be bothered to do their jobs, and ask you to do it for them. 3. Very very terrible communication, constant complaints on Ed for being unable to contact either professor or the TAs. Detailed Review: This was, genuinely, the worst class I have ever taken. It is a Frankenstein course, in which two different courses with very little to do with one another are sloppily smashed together. The first half is absolutely awful. The professor just throws up a bunch of proofs with no explanation whatsoever as to how the content is applied. There is exam study guide, just "watch the lectures and know the algorithms". The assignments are mindless code implementation of the algorithms, and since the class is designed to require as little work as possible for the staff, every single assignment is peer-graded. While this makes assignments extremely easy, it teaches you nothing and you just sit and hope that your classmates, who are likely as confused as you are, just click through and give you 3's. There is also no grace period for late homework, which is ironic because the professors are not expected to do anything on time. The second half of the course is the same, but the lectures are slightly more understandable, and the homework is considerably easier. The second exam guidance was "study everything from part 2 of the course. No book, a single page of notes". Utter garbage, and, as of May 6 (grades are due May 7), we have gotten no feedback, and 3 EdStem posts are currently sitting ignored with over 40 likes from students wanting their grades. Disgusting behavior. This course needs to be launched into the sun. Do not take this course unless you truly do not care about learning anything, and only want an easy grade.

Midway through the course right now and I'll update this review later. Decided to leave a review because there aren't many here. This is truly one miserable course - it is undoubtedly the worst course I have taken in my entire academic life. The lectures are okayish but they just go over the concepts and don't teach you how to solve problems or do proofs. The course covers a lot of ground-- on average we have about 2.5 hours of lectures a week and they are packed with new concepts and barely have any example problems. We have had a problem set due almost every week now and they have been a real task. There are questions on the problem set that are nearly impossible to solve because you won't find similar questions in the textbook and the lectures barely do any proofs. The course textbook is quite intimidating to read and since the lectures don't help much, you will have to work your way through that dreaded book. The name of the textbook is convex optimization by Boyd and Vandenberghe and is available for free online- so you can check it out. I don't understand how the previous reviews claim that they spent only 10 hrs/week because I'm easily spending 20 hrs/week on this course. The weekly work till now has been - watching and understanding the lectures, understanding the solutions released for the previous weeks problem set, grading 5 peers, deciphering the current weeks homework, trying to attend office hours, reading the textbook, reading piazza posts about clarifications regarding the homeworks (every problem set till now has had mistakes and some questions are just vague) and then finally spending hours trying to solve the homework problems. We had the midterm a few weeks ago and it was brutal to say the least. It was multiple choice so no partial credit. Most questions on the midterm were not related to the lectures, the homework or the textbook! It had many tricky questions and the options given were extremely close in correctness. To add to that, we just had one hour to complete it! So all in all, the midterm just felt like a guessing game that did not judge what we had learnt and seemed highly dependent on luck rather than knowledge of the subject. To make things worse-- 70% of the overall grade comes from such exams! The only good thing about this course is that the professor is very active on piazza and I've seen most students questions are answered within a couple hours. Seems like this course is gonna cost me $1000, a whole lot of time, points from my GPA and in the end I won't learn much either. I Definitely wouldn't take it if I could go back in time. Update: The second midterm was a disaster as well because nearly half the questions required knowledge of some obscure linear algebra concepts. The final was much better and actually had questions that were indeed from what we had learned this semester in this Optimization course (though a few questions still seemed unfair and thus purely dependent on luck). In the end, professor Caramanis was very generous and the class was curved quite heavily, but I still feel that the exams (which is 70% of the grade) were not a good indicator of how well we learned the subject and I still think that most of it just depended on luck. The all or nothing grading scheme didn't help either.

Pros: 1. Easy and simple if you have a background in machine learning 2. High level overview if you have no background in machine learning Cons: 1. Too high level to learn in-depth 2. Lectures were very bad 3. The only meaningful assignment is the final project Detailed Review: Like the title says, I regret taking this class. I don't think I learned anything at all. The 6 quizzes were easy to pass, you just need to look through lecture notes and find a matching sentence. The project was actually fun, but it's basically self guided and something you could do on your own without taking the class. The class could potentially be better if it was made more engaging + informative but in it's current state I would not take it.

Pros: 1. Too easy to get an A 2. 1 hour per week time commitment 3. TAs were helpful to some extent Cons: 1. Course material/videos are useless 2. Minimal learnings from the course 3. Waste of tuition fees and a semester if taking it as the only course in the semester Detailed Review: The course content is not structured and presented well. It was more confusing to watch the videos than to read the book. Skip videos, skim through slides to answer quizzes, read the book and watch youtube video to learn and understand the course topics. You won't miss anything but an easy credit (with an A grade) if you don't take this course.

Pros: 1. I can't really think of any 2. I can't really think of any 3. I can't really think of any Cons: 1. See detailed review 2. See detailed review 3. See detailed review Detailed Review: This course makes me really, really, sad. It makes me sad that possibly no one ever told the instructors for the course that this is no way to teach, and that they may have been taught this way themselves. It makes me sad that I can't tell whether the course is taught this way because the instructors really don't know of any better way of teaching it, or because they just don't care (and that it's hard to tell which of these is true). It makes me sad that the majority of students won't have gotten very much out of this course, despite having worked hard at it. It makes me sad that there are mistakes in the homework solutions, and that some of them aren't even deep or subtle (which may suggest that the person putting them together didn't care very much). It makes me sad that there are still so many mistakes in the slides, and that the slides are hard to read in a course which has no official textbook. It makes me sad that, by the middle of the semester, everyone was so demoralized that they were handing out full marks for peer grading simply for writing your name down on your homework (a slight exaggeration, but only a slight one). It makes me sad that the prerequisites for this course are given as undergraduate linear algebra and calculus, but the course definitely assumes prior knowledge of real analysis and even some topology. It makes me sad that this is evidently the best that we can do, here. And it makes me sad that we don't care more about students and that people who showed up genuinely excited to learn, and ready to work hard to do so, were for some reason subjected to...whatever this was. I can confidently say that this was the worst, most demoralizing course I've ever taken, and it makes me sad that I got less than nothing out of it. I feel that this course sent a really, really clear signal: You, as a student, as a person, are not important to us at UT. Your learning is not important to us at UT. Your success is not important to us at UT. We will, nevertheless, gladly take your money.

Pros: 1. Causal inference is an interesting topic. 2. Not a large time commitment. Cons: 1. The course was very philosophical. 2. HW and exam questions are ambiguous and frustrating. Detailed Review: Overall, this has been my least favorite course that I have taken in the program (out of 7 completed). The topic of Causal Inference is useful and interesting, but unfortunately, the course was taught more like a philosophy course. This led to ambiguous and confusing questions on both the homework and exams. Often, the answers to HW questions and exam questions were wrong, and the grading had to be updated after multiple student complaints on Ed Discussion.

Yes, this class is really a 5 in difficulty. Not because the content is hard. Not because the homeworks or exams have difficult questions, in the right context. This class is a 5 in difficulty because the tests for this class come from a specialized linear algebra class. Tests are 70% of your grade. This means, whether you read the title of the course or not, this is not an Optimization course. This is 70% a hellish Linear Algebra class. You are tasked with learning linear algebra. All of it. Because you don't know what part of linear algebra the test is going to cover. It feels like studying Biology and then accidentally walking into a Physics exam for the final. So this course requires 8 hrs of work per week. Because the content truly isn't difficult. But this class is also a 5 in difficulty. Because while you can study and pretend you are prepared for the exams, all you can really do is realize you have no control over you present situation, play CoD while tilting your head as to keep the tears out of the padding of your headphones, not ever review lecture material, and take the exam completely unprepared. Why? Because preparing for Optimization doesn't help you in a Linear Algebra Class. Your test grade is the same whether you've prepared or not. Because you'll never be able to prepare for obscure linear algebra without knowing which obscure linear algebra to know, and you don't. Good luck everyone. It's still a better choice than Algorithms.

Pros: 1. Bayesian distributions 2. Gibbs sampling Cons: 1. Nothing pactical 2. Theory is in areas you're unlikely to encounter Detailed Review: If there was a class ripe for overhaul, this is it. I want to compare it to NLP at a meta level, because NLP is not really the same thing but was expertly delivered. In NLP, we touched on the theory behind language, pytorch (I hadn't done DL yet), and several approaches to generating text including LSTM and transformers. We still managed to do practical projects that I'm proud to have completed. In this class we seem to have endlessly computed conditional distributions, and did a little bit of linear algebra that might be important to be aware of but I don't think so. There are fundamental aspects of practical regression modelling that were not touched on at all. There was no mention at all of the assumptions of ordinary linear regression or how to test for them. I would bet that the majority of the class's don't know what heteroskedasticity or serial correlation even are, let alone what an approproate test for them might be. We went really deep into a bunch of Bayesian stuff. I thought the math was quite interesting from an intellectual point of view, but I have no idea why you would ever do it in practise. I've done more than a few regressions in my life, but I've never approached them with an idea of a prior mean and standard deviation for the parameters. I don't know when I would ever find myself in the position of understanding the parameters so deeply but sill needing to do a regression. If I was at that point, surely I would move onto ML methods. I've seen complaints on here about classes that were just very hard, but still quite good. This is not that, it's one that I wish I could have avoided. Not worth the money or time I spent on it.

Pros: 1. Low-effort credit for the summer 2. N/A 3. N/A Cons: 1. The lectures are not helpful in the slightest 2. The slides/supplemental materials and sample code are indecipherable 3. There is zero practical application during the course Detailed Review: Others have summarized the course far more effectively than I could hope to. I will just say this: the course is as bad as the reviews indicate. Take it in the summer, grit your teeth, get an A, and move on. Search on YouTube for instructors who put more effort into free online content than this paid course.