High-Dimensional Probability

This course builds probabilistic foundations for theoretical research in modern data science. You will learn some methods that form an essential toolbox for anyone looking to do mathematical work in machine learning, theoretical computer science, theoretical statistics, signal processing, etc. The course is suitable for students in mathematics, statistics, computer science, and electrical engineering. A solid background in undergraduate linear algebra, real analysis, and probability theory are minimum prerequisites. Some familiarity with metric, Hilbert and normed spaces is a plus, but is not required.