Midterm was a copy of the review, and because the median was so high, the TAs made the final extremely difficult Class overall was a lot of work, but Prof. Kao explained the material very well and in a simple matter. Homework was pretty difficult too, and it took a long time to finish.

Kao a is an absolutely fantastic professor. His lectures are clear and engaging, and manage to break difficult concepts down into understandable chunks. He provides excellent slides, both annotated from class and unannotated originals, which are wonderful for studying. His slides often mention cutting-edge research in deep learning. Seriously, this is what a proper college class should feel like.

Although the class has listed prerequisites, they're not enforced. ECE 133A isn't really required (I didn't take it and did just fine). ECE/CS M146 isn't really necessary either, it's just background information that's mentioned in passing during lectures (I also hadn't taken it). You really do need to take a probability class though, even if it's not ECE 131A (STATS 100A or MATH 170E, etc. will do fine) or you'll be lost in the first half of the class.

The homeworks are quite time consuming, but there were only 5. They're a mixture of written math solutions and Python coding in Jupyter notebooks. It's helpful to have some exposure to Python before the class (even better if you already have familiarity with NumPy). The homeworks are pretty well spaced out, so there's plenty of time to complete them, and the TAs provide exceptional help during discussions (seriously, don't skip discussions. The TAs practically solve homework problems sometimes). Kao gives three "late days" across all the homework, so the deadlines are a little flexible.

Instead of a final, there is a final group project where you have to apply everything you learned in the quarter to a deep learning project. Kao provides a default project (in case you aren't creative, like me). It requires a fair amount of work, but it's due before finals week, so if you start early enough it doesn't interfere with studying for other classes. Getting a good group is essential.

Overall, this was one of the best courses I've taken at UCLA, and Kao is one of the best professors in the ECE department. If you're at all interested in machine learning, I highly recommend you take this class before you graduate. CS majors can probably petition it to count as an elective.

The first part of this review is to the people who are considering taking this class without prior experience. I highly recommend not taking this class unless you took M146 and have some experience in machine learning, both of which I didn't do (this my fault). The course was very math heavy at the start and Kao doesn't define many of the ML terms that he already expects you to know.

Generally, the workload is also very intense and very much requires that you have an understanding of numpy (it will be extremely painful if you do not). Luckily Tonmoy was very helpful in his office hours for the homeworks, but the homeworks will generally be awful.

The class is very theory based. You learn a lot of how neural networks functions and the function of each hyperparameter, but you won't be taught much of how to use frameworks such as PyTorch or good practices for training a model.

I also personally would have changed the grading scheme a bit. 50% for midterm is a bit excessive. There was also double jeopardy on the backprop question on the midterm: if you made the same small mistake on both sides of the backprop, you would lose the points for both sides; you could lose 4% of your total grade in the class just for making a small algebra mistake. The 2% extra credit is nice though, and the project is graded very leniently.

I don't know what more I can say that other reviews do not state already. Professor Kao is one of the most helpful and kindhearted professors I have had the utmost pleasure to learn from. His use of slides, recorded lectures, zoom livestream, have all helped me keep up with the class without having to worry about missing one or two lectures. The TAs are the best TAs I've ever encountered in my time at UCLA. Yang, Tonmoy, Kaifeng, Shreyas, and Lahari were very helpful; were straightforward with you if you got a question on the homework right or wrong (they don't dance around you and say 'hmm you might be right' or give you some BS answer), no, they help you get to the right answer if you're stuck and they corroborate you if you are correct. The midterm was hard, but expected. The questions mirrored the midterm review closely as the TAs had emphasized, and the TAs are straightforward with you if you ask a question about what's on the midterm. I asked one of them, 'is expectation going to be on the midterm,' to which they simply replied, 'yes.' Office hours were an absolute godsend. Go to them if you are not comfortable with the subject. I had satisfied absolutely NONE of the pre-reqs, so I went to OH to get the help I needed, and it WAS helpful.

I won't sugarcoat it; this class is A LOT of work. It's fairly easy to get an A, but be ready to also put in the time and effort to achieve that grade. I dedicated around 10-15 hours every week to this class (I took CM146, CS143, and DH101 as well for reference). It was highly rewarding and I learned SO much. AI was such a blackbox before I took this class; there was so much hype and pizzazz surrounding it. But after taking C147, it really broke it down into the base parts that go into building a neural network, and though I no longer look at AI mystically, I enjoy learning about it nonetheless. So, for anyone who is interested in this subject or is looking for a CS elective, take C147.

Kao teaches this well. I didn't have any of the prereqs and did fine. Just start assignments early and go to discussions. Many people did not study very much for the exam this year which is why it was lower than previous years. In my opinion we had by far the easiest exam (but the extra credit was very difficult) compared to previous years. The only prereq you really need is multivariable calculus, knowledge of what expectation is, and the most important is probably python and numpy skills. The rest will come. I wish we covered more material, lots of students asked really bad questions during class which kept us behind. Still recommend if you are very interested in deep learning. If you aren't very interested, you may not like the class.

Needless to say, Prof. Kao is an amazing lecturer. His teaching style is the best I have ever seen in the entirety of my undergrad career. But you already knew that. This is the third class I have taken with him, but unfortunately, it was the worst one. The class size (>500) made everything unbearable, as people were constantly asking questions. This demonstrably hindered lecture progress, as we ended up about 4 (?) lectures behind. These sometimes came in the "trying to appear smart in front of Professor Kao" flavor - thank you sweaty CS majors. Piazza, which is our class discussion forum, went from okay to terrible over the course of the quarter. Besides the fact that participation is hard to get because the average response time is under 5 minutes (thank you again to the literal hundreds of sweaty CS majors), people ended up mass posting random things effectively begging for participation points after the midterm. This midterm was more difficult than past years' exams, and it was changed from 30% of the grade to 50%. The midterm grades were noticeably lower, but not enough to warrant a curve which is completely fine. I just think the exam format should not be half of the grade, and it shouldn't be dependent on how much of the content from the TA's review sessions can you stuff onto your cheat sheets. I talked to a fair amount of people after the exam, and they all said that they were able to do well only because the exam questions were almost carbon copies of the TA review session questions and that they put them onto the cheat sheet.

I'm sure this class was much better with 30-200 people. ECE C143A and 102 supremacy.