This is a very tough course. It is a 4000 EECS and a 5000 EECS course which means that there are also going to be grad students in the class (not sure this helps anyone decide to take the course or not).

The course is VERY VERY VERY math heavy. There's content from math 2030, multivariate calc, calc 1 and linear algebra (ex. matrix operations, integrals, pretty in-depth probability (Bayes theorem, expected values, etc.) To be honest, be prepared to do the work of two courses, you'll need to brush up on your math and then review concepts as the classes go on since they're too complex to understand last minute. The class content is useful if you're going into the research part of ML. But if you want practical knowledge of ML then I don't think it would be very helpful since it doesn't teach how to implement ML concepts, and instead only implements the theory aspect.

Prof is pretty great! She explains really well and has office hours. She's very inviting of questions and encourages them. She also offers both virtual and in-person course delivery (will livestream class on Zoom during class) so you can ask questions in person from home. The lectures are also recorded so you can go back and watch them. She also uploads the slides before class and if she writes on them then she'll upload the updated slides after class. She will upload practice questions before the exam and then post the answers a day or two before the exam. However, in this course there weren't any concrete examples of how to solve a lot of the questions. In the lectures the professor explained the theory of how things work and the steps for how to solve a question but never actually did...read more

I wish I knew exactly how math heavy it was. I originally thought that if I work hard then I'll be able to do well. But honestly, if your math isn't very strong then I don't see how working hard will allow you to do well. The concepts are based on math and the assignments are difficult to understand if you don't know the math well. Also if you're taking it, don't slack off! You can't just watch the lectures and catch up! You need time to understand the topics!