He is the BEST professor in the ECE department! His lectures are extremely clear and engaging. Although people found his homework to be lengthy and difficult, I found them interesting as they were pretty well-written. He isn't one of those professors who test on things that they've never taught - his midterm and final were really fair and their lengths were manageable. Overall, I would highly recommend taking this class with him as it would probably become one of the most memorable and enjoyable experience in your academic career.

This class has the ability to be insanely tough. The homeworks are insanely hard, and the material covered in class is also really difficult. However, they make the midterm pretty easy, but more importantly they grade *SUPER* easily on the midterm, and the final project at the end is leniently graded as well. Hence, this class becomes a pretty enjoyable experience.

This class has a lot of practice, so if you're like me and don't understand something at first, you can try again until you get it. Each TA publishes slides + discussions + worksheets + solutions to test understanding. Then, Kao goes over lecture and has really good slides which explain wtf is going on (they are verbose enough to actually understand them without needing to hear his voice in lecture). Finally, there were many practice problems for the midterm, I think 3 practice midterms were released, which is pretty crazy compared to other classes.

In my opinion, the homeworks are super hard. I know a lot of people resorted to githubbing them, since they seemed impossible at first. I went to the TAs a lot and they basically told me the answers; the hardest part is just manipulating dimensions and stuff to make the homeworks even run.

The homeworks weren't that good, but the midterm was very fair. I didn't have any idea what was going on in the homeworks, yet easily did the midterm.

Ultimately, a good class that isn't that much work unless you don't utilize TAs at all, in which case it becomes super hard. The midterm is fair and I feel like I gained a good amount of experience in deep learning, and now know how to kinda use PyTorch / TensorFlow. And Kao has no accent! So I can actually understand him, I love other CS profs too but sometimes I have no idea what they are saying. 10/10 class but I wish there were more written homework questions instead of just coding all the time.

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.

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.

I think there is actually very little you need to know on whether you should take this class. Know Python (if you do not, homework will take a lot more time than it should). Be familiar with manipulating arrays and lists and especially with numpy functions. You can definitely pick it up as you go, but it will cost some extra time. The homework are difficult sometimes, but Kao will give you everything you need to know to answer questions; if not him, the TA's. Kao is probably the best professor at UCLA and his lectures are actually the most engaging and inspiring things to listen to. He keeps the students engaged, answers any questions, but most importantly, he shows that he cares. He is not some professor that is pompously concerned about their research that they view teaching as a second priority. Kao shows that he cares about teaching and I think that is all the reason you need to take this class. It is a lot of work do not get me wrong (in fact he will tell you this before hand); be familiar with Linear Algebra and Probability and you will end up with an A if you do the work and understand the concepts.

I took this class as an add-on to ECE 102 with Professor Kao. It was just a one-hour seminar every week and then a project at the end. The project wasn't too bad and just involved some Matlab programming. Overall, I would recommend this seminar if you need some honors credit.

Professor Kao is a great lecturer and one of the best professors I've had in the ECE department. This lectures were clear and the homework really aided in understanding the concepts covered in class. The midterm and final were difficult but reasonable, and I would highly recommend taking Kao for 102.

This guy is the ABSOLUTE GOAT. Don't hesitate to take this class. He is one of the best lecturers I've ever seen and he knows how to really make you interested in the material. Office hours are super helpful and the TAs were also great for my quarter. Grading is extremely fair and often times quite generous. You can tell he really cares about his students. Homework is worth doing, not only because it is a fair chunk of your grade, but because the homework problems are interesting, engaging, and not at all tedious. Overall, I recommend Kao more than any other professor I've had yet.

Professor Kao is great. His lectures are very clear and he works through problems carefully on the board, which makes it easy to follow. He is nice and approachable. He answers questions directly and clearly in class.

The material of the class is tough. The homework will take a long time, but the lectures, discussions, and office hours will help guide you through them. Everything is well organized online and all the resources (including previous tests) were given to us. The final exam was fair and overrode the midterm grade, which was tougher our quarter.

The only complaint I have is of the TAs. They were pretty good, but they each had their faults. Siyou was kind but unclear with her work. Sometimes I couldn't understand how she arrived to her answer because she would not write down much. Still, her grading was much more organized and gave lots of partial credit. Tonmoy was very clear and wrote down great notes from his discussions, which were informative and easy to follow. The issue I had with Tonmoy was that he was harsh. He did not have enough patience with students and would snap at them if they did not understand the material. His grading was often all or nothing. For such a math-heavy class, this seemed excessively harsh.

All the instructors were well-intentioned and the class was a good experience overall. I learned a lot and I would definitely recommend this professor.

This is probably the best introductory class to neural networks I have attended. It is very well structured and everything is built up in a purposeful way. It goes into the mathematical calculations behind deep learning, where the homework is about deriving and implementing the backpropagation algorithm for different neural network layers and functions. There are many opportunities to ask Prof Kao and the TAs questions during lectures and discussions, which they usually answer in meaningful ways.

On the more mixed side, the lectures in class are long (2 x 2 hours per week, not including discussions) and in this quarter, fell a bit behind schedule. As a result, many assignment deadlines and other topics had to be moved around, which could sometimes be confusing. Also worth mentioning is the workload, which is very high. A lot of derivations in the homework will require taking derivatives with respect to matrices, which can lead to tensor-sized derivatives easily. Coding the derivations in a performance-friendly way is also not trivial. As such, there is no shame in asking for help from students and teaching staff and it is generally encouraged, so you do not spend many nights figuring it out alone.

While the material is challenging it is communicated well and I personally feel I have a much better of what is happening when using neural networks from frameworks such as TensorFlow or PyTorch.