EE 133A is by far the hardest class the ECE department has to offer. However, this class has a ton to offer and is probably the most useful math class you will ever take if you want to go into machine learning. This class is pretty much an advanced version of Math 33A. If you like linear algebra, then you would enjoy this class.

Professor:
His lectures are some times boring, but I attended every lecture and did not have to read the textbook at all. As long as you understand what's going in the PPT slides, you should be fine.

Homework:
The professor assigns weekly homework assignments. Do go to discussion sections because the TA usually solves half the homework problems. Attend either the TA's or Professor's office hours because they will walk you through how to solve the homework problems. The Professor and the TA know their homework problems are difficult, so they do not expect you to solve the homework quickly.

Exams:
The midterm was doable, the final was beyond impossible. As others said, there are tricks when solving homework problems. The exams will be similar to the homework problems, except you have to think of these tricks on the spot.

Grading:
The median for the midterm and final hovered around a 50%. The Professor curves the median to a B+, so half the class will get at least a B+.

Vandenberghe is a good professor and this is a cool class.

This class is so much fucking work lmfaooo. Get ready for a massive info dump

This is the driest class I have taken at UCLA and do not recommend it. The curve is very good at the end but it's not worth it. Save yourself and if you want to preserve your sanity, don't take this class.

Vandenberghe is a solid professor and overall good guy. If you ask him clear questions, you should get clear answers from him pretty quickly.

As others have mentioned, he is not an energetic lecturer. I think I would've been able to pay attention in this class better if it were my first class of the day, but it was not. Vandenberghe's voice nearly lulled me to sleep several times during the quarter.

Something I can't understate is how accommodating Vandenberghe seemed to be in the wake of weird pandemic-era classes. He gave us many options for following lecture and taking exams. I really appreciated that.

This class covers some seriously tough material, but you will pass if you make a good effort. He curves generously if the exam scores are low (which they often are.)

This is one of the most interesting classes I've taken at UCLA, and definitely the most enjoyable one. Professor Vandenberghe is a REALLY good lecturer, and the homework problems he wrote are hard, but certainly one of a kind - they were hard, but not the kind that might make you want to jam your laptop. I genuinely enjoyed these problems. The grading scheme was also really generous, and the final project was basically free points. Overall, I would highly recommend taking this class with him.

This was an extremely challenging class. Probably the most challenging among the core upper division EE classes. The class consisted of 7 homework assignments, a midterm, and a final. The homeworks were very tricky and took a lot of time to understand. Most of them took upwards of 6 hours to complete (in my and many others' experiences). Many of the homework questions are just impossible to understand without knowing some trick that you wouldn't think of immediately. Going to TA office hours and discussions are necessary if you want to do well on the homeworks. Course hero was also an extremely great resource because many homework questions are recycled from previous years, so they can be found there.

Vandenberghe's lectures were quite dry. They mainly consisted of him reading off of his prepared lecture slides and writing some annotations on the chalkboard. He is pretty monotone and tends to mumble a bit. However, Vandenberghe is an extremely great guy with a good sense of humor. He is very concerned about the students' success and is very helpful in office hours. I would give him a soft recommendation as a professor for this class.

The material was extremely uninteresting in the beginning. It mainly consisted of a review of concepts from Math 33a (definitions of vectors, matrices, and operations). However, it got much more interesting later on as we talked more about regression and how the concepts we were learning were applied to fields like machine learning, image processing, and more. While the material is quite dry at times, it is beneficial and useful to know if you have any interest in continuing studies in Machine Learning and related fields.