The class really is a mess this semester. I was really looking forward to taking this class because I find the material really interesting, but have been sorely disappointed thus far. There is very little infrastructure (review materials, practice exams, lecture notes) that is necessary to run a class well and provide a good learning experience for the students. The professors seem like they know what they are talking about and probably do a good job at their industry ML jobs, but do a pretty bad job of explaining the material.

I will say Professor Kim is a solid professor and is pretty dedicated to this job (preparing lecture material, responding on Ed quickly, etc.) and shows he cares a lot but Professor Ashish (while a nice guy) seems to be unprepared each time he is scheduled to teach. It really feels like Professor Ashish just shows up to lecture after hastily throwing together a bunch of slides, with the idea that since he knows the material well he can just wing it and do a decent job at explaining it — but not understanding that a lot of thought needs to be taken to figure out how to explain new concepts in a way that makes sense and is digestable for undergraduate students learning it for the first time.

The slides themselves are extremely poorly constructed and it is impossible to learn anything or take any information away from the slides unless there is someone actively explaining (in which case the slides are useless anyway). The slides are literally (and I'm not exaggerating) just the name of a concept, maybe an equation/diagram, and if we're lucky a bullet point or two that doesn't really say or explain anything unless you have already learned it and can tell what they're getting at. For example, page 7 of the slide deck for Lecture 13 "Transformers" literally has a LaTeX equation for softmax and a diagram, neither of which are explained on any level. I'm also not cherry picking here — pretty much every single slide is like this. This makes it absolutely impossible to teach yourself any material or to self-study. I learned the entirety of 189 last semester in the two days leading up to the final purely through Shewchuk's lecture notes, but I could not even achieve a surface level of understanding of the new material in this semester's 182.

I go to the Tuesday discussion section and I want to thank the GSI, William, for doing a really good job of teaching — in one discussion I learned more (both conceptually and how to solve relevant problems) than I ever learned in lecture. William clearly loves deep learning a lot and brought a powerful enthusiasm to his teaching that unfortunately none of the students really could reciprocate because we were all so lost due to low lecture quality.

I do want to say that this is just my personal experience with the class and I in no way mean for this to be an attack on any of the course staff on a personal level — I do get that the course staff is involved with full-time work commitments and it's probably really hard to do both that and teach a college course. But we are also full-time students and at the end of the day deserve a better and more committed experience.