Because many students like to read ahead, here are draft lecture notes for the full course. These will be updated as we actually go through the content during the semester, but this should give a good idea for someone who wants to prepare before class.
If you find any errors in these notes I will still try to correct them. Anyone should be able to leave comments.
These notes were primarily the effort of Smriti Murali, Daivat Bhatt, Christopher Nota, Ashish Singh, Dmitrii Petrov, and Justin Domke, as well as incorporating some figures made by TAs in previous iterations of the course.
03 - More Broad Concepts and K-NN Regression
07 - Capacity and Linear Regression
08 - Ridge, Lasso, and Regression Trees
09 - Visualizations and Regression Trees
10 - Model Selection and Cross Validation
12 - Cross Validation and Classification
13 - Classification trees and losses
14 - Linear Classification and Autodiff