This course is a great introduction to Machine Learning. The content covers a lot of useful topics and you learn the mathematical theory behind a lot of popular algorithms. After a few lectures only about 1/3 of the class shows up to lecture. Admittedly, professor Qi is a really dry lecturer and I couldn't bring myself to pay attention in the class. You can get most of the content just from reading the slides, which are posted online (along with the syllabus, just google the course name). The homeworks are really fun. Each homework covers an overarching topic (e.g. regression - normal equation, GD, SGD, neural networks, SVM, etc.). The homeworks are a mix of coding and "sample exam questions" which are concept questions about the topic in the homework. She gives a lot of opportunities to get extra credit on the homeworks and she extends the deadline for most homeworks cause she's really nice. For example one of the homework assignments was classifying some labels in a data set using a neural network. She made the homework "competition style" where the top 20 submissions get extra credit.

The tests are trivial (one midterm and one final); they are similar in style to CS 2110/easy CS elective tests. No coding on the tests, it's basically all understanding concepts and explaining how things work.

Overall would recommend this class! One of my favorite CS electives so far.

This was my favorite course I've taken at UVA. Yanjun Qi is extremely good at making a dense topic very accessible. if you are interested in machine learning take this course, you cover many of the popular algorithms, learn the math, and learn how to use them in Python. Highly recommended