Lecture notes for Machine Learning, COMPSCI589, taught by Justin Domke at UMass Amherst in Spring 2021.
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 assistants in previous iterations of the course.
If you find any errors in these notes I will still try to correct them. Anyone should be able to leave comments. (At some point they will become "frozen" and commenting functionality will go away.)
01 - Introduction - Feb 2, 2021
02 - Broad Concepts - Feb 4, 2021
03 - More Broad Concepts and K-NN Regression - Feb 9, 2021
04 - K-NN and Matrix Calculus - Feb 11, 2021
05 - Matrix Calculus - Feb 16, 2021
06 - Linear Regression- Feb 18, 2021
07 - Capacity and Linear Regression - Feb 23, 2021
08 - Ridge, Lasso, and Regression Trees - Feb 25, 2021
09 - Visualizations and Regression Trees - Mar 2, 2021
10 - Model Selection and Cross Validation - Mar 4, 2021
11 - Cross-Validation - Mar 9, 2021
12 - Cross Validation and Classification - Mar 11, 2021
13 - Classification trees and losses - Mar 16, 2021
14 - Linear Classification and Autodiff - Mar 18, 2021