I love Jonathan Kao. His lectures are very clear—with amazing annotated notes. Concepts that may seem confusing—Kao has a unique ability to make them seem approachable and common sense-like.

In office hours, he is always willing to take questions, talk about the course, or just life in general. I've had great conversations with him regarding the existence of free will. Not many professors are that engaged with their students.

One minor criticism I have of Kao is how he takes questions in lecture. He indulges in almost every single question, which slows down lecture tremendously. It's great he wants to resolve any unanswered questions, but it's just too many. (It's also evident that some students ask questions just to make them seem smarter to the professor, but that's another concern.)

I feel like the professor can fix this by setting expectations for questions at the beginning of the course. If you feel like your question helps everyone in class, feel free to ask it in lecture. If not, ask it during office hours.

Overall though, great professor.

I love Tonmoy Monsoor. Super knowledgeable TA, always willing to help during discussion, holds great review sessions.

As all the other reviews state, this class is goated. I petitioned for it to count as a BioE major field elective. For all the non EE people thinking about taking this class: you gotta at least be familiar with the pre-reqs for this class or ur gonna get rekt by all the probability and linear algebra.

Hands down my favorite class I've taken at UCLA so far. A mix of neuroscience, signal processing, and ML, it really teaches a lot of useful skills that can be used in a lot of diffferent fields of EE and CS. Homeworks are hard and primarily math focused or coding in python and numpy focused, and are *very* time intensive, don't start too late! Kao's 4 late days policy saved me a couple times for sure though during the quarter. Tests are also a doozy, but shoutout to the TA Tonmoy for his goated review sessions and Kao for providing all the previous midterms/finals to help you study.

A lot of the tools that we've used in this class: numpy, signal processing/filtering, clustering, have come up for me already in EE research and throughout CS internships so this class has definitely helped a lot in terms of experience that can be used in industry. The class material itself is extremely interesting to me as well so I'd definitely recommend it!

Kao is, hands down, the best professor in the ECE department. 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. Kao is absolutely a subject matter expert, since the course focuses on research advances that he was a part of. He can answer literally any question on his lecture material. Seriously, this is what a proper college class should feel like.

A probability prerequisite (not necessarily ECE 131A, but any equivalent class) is absolutely required, and you may struggle without it. Much of the second section of the class focuses on poisson processes, and a course in probability is essential. It would also be helpful to have some knowledge of Python beforehand, since the homeworks generally assume it. However, you don't need any knowledge of electrical engineering at all. There's a tiny section on equivalent circuits in the first part of the course, but you don't need any background knowledge to understand it.

This class is a lot of work. Kao isn't kidding when he tells you that in the first lecture. The homeworks took a long time each, even though there are only 6 of them. They're a mixture of written math solutions and Python coding in Jupyter notebooks. 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 four "late days" across all the homework, which is an exceptionally generous grading policy.

The tests are difficult, but generally the class average is very good (attribute that to Kao's exceptional teaching abilities). He posts plenty of practice tests beforehand, and the TAs host a long review session for each test, so there is plenty of practice material. Both the midterm and the final had a bonus question for extra credit, but the bonus questions are generally harder than the rest of the test.

Despite the workload of the course, I would absolutely recommend it (and for CS majors, you can petition it to count as a CS elective). This course was one of the best courses I've taken at UCLA, primarily because of Professor Kao. It's a genuine pleasure to take his courses. Even if you have little interest in neuroscience or brain-machine interfaces, you will probably still find this course more engaging than most of the other courses offered at UCLA solely because of Professor Kao.

This has been my favorite class. If you're interested in neuroscience, machine learning, signal processing, brain-machine interfaces, or if you just need an interesting EE elective, this is the class for you. Most of the class was EE majors, but the course is incredibly useful for anyone interested in neuroscience and engineering. Professor Kao is one of the best instructors I have ever had: the lectures were very engaging and clear, the homeworks were well-written, the exams were fair, and the office hours were helpful.

The first third of the class focuses on basic neuroscience, which requires no prior background. The last 2 thirds focus on modeling neuron activity and then turning brain activity into actions in real life (i.e. moving a cursor on a screen or something similar). In order to do well in the class, you just need to be decent at calculus, linear algebra, Python programming, and probability theory (it will be very difficult to succeed without these prerequisites). The homeworks can be tricky, so do go to Jonathan's or any of the TAs' office hours since they are also very helpful. Most of them included a Python (Jupyter notebook) component which made them slightly time-consuming, but still very doable (and fun!).

Almost everyone I met in the class just took it because of Professor Kao's teaching and lecture style (annotating detailed lecture slides from an iPad), and walked away with an appreciation for the field.

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.

Kao is one of the best professors at this school. He is clear, engaging, informative, and an incredibly supportive teacher. Would highly recommend both Kao and this class, and would easily choose to take this class again.
If you've had 102 with Kao, his style of teaching for this class is very similar. He posts unannotated slides before lecture, annotates them during lecture, and reposts them to CCLE afterwards. His unannotated slides contain information and videos, but most of the derivations and math he does by hand on blank slides. He uses polls to monitor class comprehension, and frequently stops to ask for and take questions. He uses piazza to allow students to answer each others questions, but both Kao and the TA's are present to resolve ongoing confusion.
The neuroscience and probability homeworks were written, while the decoding and classification were jupyter notebooks. They walk you through complex concepts in small increments, and are interesting and fun to work through (and great for learning python!).
Brain machine interfaces were one of the first things that drew my attention to electrical engineering, and I found it incredibly interesting to take a class in exactly that. If you have the opportunity, I would highly recommend taking this class.

This is the third class I've taken with Professor Kao, having taken his Systems and Signals, and Neural Networks and Deep Learning classes. As always, Kao's lectures are very clear, informative and interesting.

The HW can be a bit tricky- start ahead of time and go to office hours. Throughout the quarter, Kao was very accommodating and even relaxed the grading scales at the end of quarter.

Due to COVID-19 and remote learning, we weren't able to cover the amount of material the class usually covers. However, for context: the first third of the class covers basic neuroscience, including action potentials and how the brain works. The second third covers Poisson processes and discrete classification. The third half covers decoding including Wiener and Kalman filters, which I think is the most interesting part of the course.

If you are looking for an interesting and useful elective, this is the class for you. Highly recommended- 10/10.