Machine Learning

CS760, Spring 2021
Department of Computer Sciences
University of Wisconsin–Madison

Logistics Info

Course Description

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to distinguish between images of cats and dogs, analyze the sentiment behind text data, and play games like Go and Starcraft). This course provides an introduction to the theory and practical methods for machine learning, and is designed to give a graduate-level student a thorough grounding in the methodologies, mathematics and algorithms of machine learning. Topics covered include nearest neighbor method, decision tree learning, Support Vector Machines, Bayesian networks, neural networks, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, etc. Assignments include some written exercise and short programming experiments with various learning algorithms.


Students entering the class are expected to have a background knowledge of probability, linear algebra, and calculus, and have good programming experience. The course will not provide a review on the background knowledge, or tutorials on programming.