If you have taken Dr. Kao for EC ENGR 102 the teaching style is very similar, posted lecture slides and after class annotated slides are posted. He is a wonderful professor and will happily answer any question no matter how dumb it is. Dr. Kao takes time to make sure students understand the material before moving on and engages with the class by using polls and asking students to raise their hands if they understand the material to gauge how things are going.

Python is a big part of this class as the majority of homework requires a significant amount of programming , homework is worth 40% of your grade. Do not slack on the homework and seek help early, missing or not doing well on one homework assignment will greatly affect your grade. My class had 6 homework assignments but in the past there have 7 since they covered more material.

The final exam and the midterm were very fair, if you know how to do the homework and you study the past midterms and finals you will do fine.

In summary, this class is time-consuming and can be pretty challenging at times but the material I learned was very interesting and I very much enjoyed it. You won't find another professor like Dr. Kao.!

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.

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.

This was a difficult class and quite heavy on stats/probability. In my experience the course was both super interesting at times, and super painful at others (especially with over 2 lectures spent on deriving the Kalman filter).

The first couple weeks cover mostly biology concepts about the brain and neurons, including a couple resistor/capacitor equations. Then the rest of the course is a lot of derivations to apply statistical models to neuron activity, including a couple ML algorithms.

The homework involves both a lot of math/stats/probability problems, as well as Python labs to implement the models covered during lecture. I enjoyed the labs the most, since they allowed us to put the theory into practice on real monkey neural data.

Some tips going into this class: Kao's exams are very similar to the practice review given beforehand (prioritize studying and really understanding these). Also discussions and TA office hours are very useful, especially towards the second-half of the quarter. Often times, they will give you direct help with the homework problems and Python code. Finally, start the assignments early as they are not usually completable in one day.