Machine Learning Theory

Catalog description

How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering this question. This course will cover fundamental concepts and principled algorithms in machine learning, particularly those that are related to modern large-scale non-linear models. The topics include concentration inequalities, generalization bounds via uniform convergence, non-convex optimization, implicit regularization effect in deep learning, and unsupervised learning and domain adaptations. Prerequisites: MATH 51 and STATS 117 and either CS 229 or STATS 315A. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.