Course: Machine Learning (COMPSCI 589, Fall 2024)

Course staff:

• Instructor: Justin Domke
• Teaching Assistants: Ke Xiao (email: firstname + last name all together in lower case @cs.umass.edu) and Abhinav Agrawal

Time: Asynchronous

Course Description: This course will introduce core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. On the theory side, the course will focus on understanding models and the relationships between them. On the applied side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, design of experiments, and presentation and interpretation of results.

Lectures: Lectures will be pre-recorded and made available on electronically.

Tentative course schedule:

• Bayesian inference
• Unsupervised learning
• Regression
• Classification
• Model selection
• Automatic differentiation
• Neural networks

Textbook: The course has no mandatory textbook. There will be optional readings from:

• The Elements of Statistical Learning by Hastie, Tibshirani and Friedman
• An Introduction to Statistical Learning by James, Witten, Hastie and Tibshirani

Grading Scheme:

• Homework: 100%