STAT 4630

Overall a pretty disappointing class, I don't really feel like I learned anything substantial this semester. Machine learning as a subject is really fascinating and has a lot of potential, but the way it was taught in this course wasn't very effective at actually helping us grasp its core concepts. The lectures mostly just consisted of reading off the slides, and the presentations themselves were mostly theory-based and were not really relevant to the homework/project assignments, which were mostly R/Python coding. Half of my assignments weren't graded until the very end of the semester, feedback was very limited and the grading in general seemed harsher than it needed to be (especially on the project). Overall I would not recommend this class unless you absolutely have to take it for a major/minor requirement.

Just an incredibly frustrating course overall. Not because of the difficulty of the content (which wasn't really that bad), but because the class itself seemed intrinsically designed to obfuscate any and all logistics and information.

Professor Yu seems nice but there is definitely a language barrier that makes it hard to understand the material. She's rather quiet too and talked into the whiteboard a lot, so lectures were unhelpful and very rarely had any benefit for the labs or homework assignments.

Assignments took weeks or even months to be graded, and the feedback, if there ever was any, was unclear and inconsistent. When my group met with Professor Yu to discuss the first part of our final project, she had only one minor thing to say about our report despite taking off a significant amount of points. It was also never clear what was assigned or when it was due, as the information on Canvas was changed constantly.

I took this class as a requirement for the Data Analytics minor, and I was really looking forward to it, but I can't say that I came out of it with any meaningful understanding of the material.

Ok so, the course content was very very interesting. I really enjoyed learning about so many ML models from a statistical perspective (although I thought it was slightly theory-heavy). Some of the lectures might get boring and dry, and it would be nice to have some more guidance on the coding portions in addition to the labs. I thought it would also be nice if there was more Python usage past just the NN lab and HW, like maybe even allowing students to do assignments in either Python or R. There are 5 HWs, a 2-part project, and some labs (completion grade for participation), so the workload isn't that much.
My only 2 complaints are: It would be nice to have more timely grading, and more effective feedback on assignments, especially the project.

This course was very disappointing for me as they content was more about theory than application. Lecture attendance is not necessary as the theory covered in lecture is not what the assignments are based on. My experience with Prof. Yu was different. She was not very good at communicating expectations with students and gain student participation. Most of the time, she would turn around and talk to the board. The assignments in the course are homeworks and a group final project, which you will get very minimal feedback on. Ultimately, do not expect to understand the machine learning techniques you go over and I am not sure if I would recommend this class.