CS760, Fall 2021
Department of Computer Sciences
University of Wisconsin–Madison
This course is designed to give a graduate-level student a thorough grounding in the methodologies, mathematics, and algorithms of machine learning. Topics covered include supervised learning (neural networks, support vector machines, generative/discriminative learning), unsupervised learning (clustering, GMM, PCA), and reinforcement learning. The course covers theoretical concepts such as inductive bias, geeralization, 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.