I don't have anything to say that others haven't already said, Professor Kao is truly one of the best lecturers at UCLA and I would highly recommend this class if you are interested in Neural Networks and Deep Learning. Also, the TAs for this class were amazing, especially Tonmoy Monsoor. Tonmoy is insanely knowledgeable about the topic and his discussions were super useful for the homeworks!

Grading:
Homework: 40% (5 homeworks)
Midterm: 30%
Final Project: 30%
Extra Credit: 0.5% for filling out class eval, up to 1.5% for participating on piazza (in a useful way), and some extra credit given on the midterm (final question on the exam is optional extra credit)

I would highly recommend this class to any interested in deep learning and machine learning. Professor Kao is a very good lecturer and he does an amazing job explaining concepts. I never truly understood how backpropagation worked until he explained it in class. Anyone interested in research/ML should definitely take this class. You will learn so much.

However, the class is not a cake walk. It's actually fairly easy to get a good grade in this class as long as you put in the effort. There is only one exam around week 8, which won't be bad if you pay attention to lecture (our average for the exam was a 95%). The homeworks are the real killer and can take a very long time. You essentially have to build neural networks from scratch using Python and Numpy.

Overall, this is an amazing class where you can truly learn so much, but at the price of many hours of homework. Professor Kao is probably one of my favorite professors I have ever had at UCLA.

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.

Professor Kao is super helpful and always willing to answer questions in class, during the break, after class, and in office hours. I personally did not take the pre-requisites for this class, so as long as you're willing to put in the time and effort, this class is great. Homeworks are quite challenging and vary in difficulty, but TAs were always willing to help, and a lot of people on Piazza had similar issues. The class is fairly math heavy, but basically everything is reviewed in the first 2 weeks, and you can spend extra time catching up on things you are a bit shakey on.
The midterm was very reasonable, and had an average of I believe 94%? Kao provided past year midterms, and I would say they were extremely representative of the actual midterm, so I was able to finish in the allotted time (with 30-45 minutes to double check my work).
The project was difficult in my opinion, since it's hard to know what architectures would work and perform well without actually implementing them and seeing how they perform, which is time consuming. Definitely try to get a good group so it's easier to distribute tasks and try various architectures.

the material taught in this class were definitely a lot more difficult than m146 or 145, he goes a lot more in depth into neural networks and cnn which is def helpful if you're looking to get an idea of what ML is rlly about. this class is very math heavy and i highly recommend taking m146 or 145 before this class.

jonathan was a great professor who rlly cared about his students and is great at explaining challenging concepts. the hws were challenging, but the TAs are rlly helpful and if ur rlly stuck, there is always github.

the midterm was challenging, but doable if you pay attention in class. the final project was graded relatively easily if the TAs can see that you put in an effort and achieve a better performance than the baseline.