Based on 8 Users
There are no grade distributions available for this professor yet.
Sorry, no enrollment data is available.
I usually don't come on this website often, but I am shocked by the reviews of other students. I will say this. Professor Grover is not the most engaging or humorous teacher, but he cares extensively to how well you can learn, much more so than other professors I have had.
I think his classes are at a great pace, he teaches extremely clearly, and all assessments are very reasonable. His office hours were great fun, we talked about many higher-level concepts not necessarily in the course, and on a personal level he's a great guy. I truly enjoyed this course.
-Leeland (if my name gives this review more credibility)
Professor Grover was an amazing professor. If you ask him questions and go to office hours he will take the time to make sure you understand. Be careful, the homework is pretty challenging but I really found that understanding the homework made sure I fully grasped the materials previously taught. At the end of every second lecture, he would also present on state of the art ML which I always really enjoyed.
TL;DR: Every lecture is very boring because he just reads the slides and he even copy other institution's HW problems. Yes, this is plagarism because he did not SITE where he got the HWs. Talk about hypocrisy. Avoid COM SCI M146 and take COM SCI M148. You learn the exact same material, except you do not have to do math proofs and know the equations on HWs and tests. In M148, you just need to know how the algorithm works, which is honestly a better way of judging how you learn these materials rather than just copying down equations and doing bullshit math. I am telling you, please, please, please avoid M146 and just take M148. Regardless of the professor is good or not, TAKE M148.
I would highly, highly, highly, highly, highly NOT recommend take COM SCI M146. Espescially with this professor. I am not sure how he got popular through social media and youtube, but all he does in lecture is just read the slides. The slides aren't even helpful by any means. He's not even helpful by any means. If you need help with HW, you can't use the lecture slides. ODDLY enough, we have to rely on the internet to get our answers even though this professor said in one class that he highly suggest to not depend on the internet, when a lot of the HW has very confusing questions and wrong formulas. Oh wait, the formulas on the slides is not correct, and we have to use the internet.... Oh, and he even said do not plagiarize our HWs when this professor literally COPY AND PASTED other institution's Machine Learning HW. Wow, what a hypocrite! It makes me very skeptic of his past works and questioned me how he is a Machine Learning Specialist.
Oh, on the side note, do not take COM SCI M146 regardless of the professor. Whether it's with professor Grover or not, there is literally NO COMPUTER SCIENCE aspect of this course. It's just a bunch of equations, linear algebra, and actually just a bunch of glorified statistical models. Honestly, this class should not be COM SCI, it should belong in MATH or STATS. Again, there is NO COMPUTER SCIENCE aspect of this course. If you really want to learn "Machine Learning", save your ass and take COM SCI M148. It's a data science course and you learn the EXACT same material as M146, except you DO NOT have to know the nitty-gritty and fatty equations and glorified math stuff. HWs and projects in M148 is way, way more enjoyable than M146's. I do not think anyone wants to do math proofs for the entire quarter than just doing easy python coding. Unless someone is a math major or someone loves doing proofs, you should just avoid this class in general. Stop going through the math and start learning real Computer Science Material. Hell, I would even suggest to take the compiler class. That is real COMPUTER SCIENCE, not a glorified statistic class.
I loved the class and easily rate it as one of the best CS classes at UCLA! This is one professor who really cares about teaching and going all out in ensuring that students fall in love with machine learning (ML). The material was dense but the way the professor went out of his way to explain it during lectures and answer queries after class is impeccable. The homeworks and exams were also interesting in testing for an in-depth understanding of the material as opposed to a shallow memorization of facts. Unlike other introductory classes at UCLA where professors take the easy route and recycle content from 5-10 years ago, this class was very well structured and gave me a holistic perspective of both the fundamentals of the field as well as modern takes on cutting edge ML in the real world.
This course exceeded my expectations and turned out to be the most enjoyable course I have taken this quarter. Despite my initial low expectations due to the math-heavy component and lack of interest, I realized that the instructor's quality is far more important than the course content itself. Prof. Grover has been one of the best professors I have had in computer science. This is by no means an easy course, but his lectures were well organized and he was very receptive to student feedback which meant so much to me. It felt like he genuinely cared about student interest and success which made this the most enjoyable course I have taken this quarter. His enthusiasm and expertise created a memorable and effective learning environment with clear lectures that made complex concepts approachable and exciting! One of the most remarkable aspects of Prof. Grover's teaching was his ability to draw connections between various topics, providing valuable context for understanding how they relate to each other and the broader field of Machine Learning in 2023.
Prof Grover is a slightly below average professor. His lectures are generally uninteresting. He practically reads directly off of the slides - which is good, because you can't hear him in a large lecture hall, so if he said anything else it wouldn't be helpful anyways. At least the slides are usually posted on Bruinlearn so you can follow along, but lectures don't really add a whole lot.
The homework is absolutely terrible. The written problems are fairly math-heavy, but nothing too bad if you've taken calculus and linear algebra. The problem is with the coding sections. The instructions on the handout and in the Jupyter notebooks often differ substantially. It seems clear that at least the coding portion of the homework was stolen directly from some other instution - probably CMU or Stanford if I had to hazard a guess - with significant portions cut out (but the instructions not updated). That's extremely discouraging, and feels like it borders on academic dishonesty. If I submitted the answer keys from the homework from wherever it was taken from, I would be referred for plagiarism. Ultimately, this results in homework that takes several hours, not because of the difficulty, but because it takes hours to understand what the instructions are actually trying to say.
The tests were probably a little more difficult than the average CS class, but not deathly hard if you actually put in some time to study for them. The math in them wasn't difficult, just basic calculus (Lagrange optimization) and linear algebra (matrices, orthonormal, matrix composition). There wasn't really any probability knowledge required for the course either.
The course content was mostly focused on motivating the machine learning algorithms discussed, and deriving them semi-rigorously. That's important knowledge to have if you want to go deeper in machine learning, especially at the graduate level. If you just want a cursory exposure to machine learning, then despite this course's name, a better course would be CS M148, which doesn't go into the same mathematical depth. If you like M148 (or if you already have an interest in and knowledge of machine learning), then take CS M146.
Perhaps the only saving grace for Grover, however, was his very generous grading policies. He allowed 6 late days across 4 homeworks, and the final grades were generously curved based on the final performance. However, he didn't release any grading scheme during the quarter, which was quite unhelpful.
This class has destroyed any possible interest I have in machine learning. I initially came into the class eager to learn. Then, shit hit the fan.
Lectures: He's a pretty decent lecturer, but when he started getting into very detailed math, I was completely lost. A few weeks in, he began giving a quick demo of cool modern day ML applications each week, which was fun to see the bigger picture of what we were learning. Still, so much of the content flew over my head.
Homeworks: Overall, there were many typos and the lectures did not help at all. The homework, which contained coding portions and free-response, was clearly copied from other sources. They took a long time and did not seem to help prepare for the exams, especially because the exams do not test any coding whatsoever. Also towards the end of the quarter, the TAs and instructor selectively answered questions on Campuswire, leaving 20+ questions about the HW/class unanswered.
Exams: The midterm had so much math that I had absolutely no idea how to do. Sure, the class said we needed linear algebra as a prerequisite, but the entire class seemed way too math heavy for a computer science class. The final was a disaster and I felt completely unprepared.
The only plus side is after failing the exams, the curve was thicc so bless Grover for saving my grade. I have no idea how he curved the class but I am surprised I passed the class, let alone got the grade I got. My recommendation is to not take this class with Grover, or better yet, not take this class at all. I'm sure there are better electives taught by better professors.
This Professor's tests are literally from math hell. Unless you are Einstein level IQ, God tier linear algebra, or been doing gradients since the 2nd grade, you will get put in your place. He's a decent lecturer, but then he just hits you with homeworks, midterms, and exams that are literally the difficulty of what he lectured on +100 levels. They were just as time consuming if not more than CS35L or CS31 homeworks. Wondering how to do them? Don't look in the slides, you won't find anything there. If you are good at linear algebra then most of the math won't be as bad. The material itself is incredibly interesting though.