Yu leaves much to be desired in their teaching approach. Classes often felt disorganized, with a lack of clear objectives or structured material. Key concepts were often rushed through or skipped entirely, making it challenging to understand the course content fully.

Best lecturer in pstat department.

I took this class as an undergrad. I found this class pretty interesting. The grading is 45% hw, 45% project, 10% lecture scribe. You're expected to use LaTeX in RMarkdown for homework and LaTeX for scribing (only one lecture per person.) The homework assignments are pretty challenging but you have plenty of time to do them cus they're only 3 of em

Class is easy for a graduate course, a lot of content overlaps with the stuff for PSTAT 131 students, except for the final that has a proofs section a little bit harder but still doable for a grad student. The final project's instructions were vague but my final project was dogsh*t and got an A- so he's chill. If u want easy A, take coburn instead

Guo seems friendly and approachable at first, as well as knowledgeable in machine learning concepts, but his teaching style doesn't convey that much. Sections seemed practically useless, lectures focused too heavily on theory with little to no explanations, and course expectations, like for the final project, were outlined weakly and much too late.

do not take.