MATH 7740

Statistical Learning Theory

Course Description

Course information provided by the 2025-2026 Catalog.

Learning theory has become an important topic in modern statistics. This course gives an overview of various topics in classification, starting with Stone's (1977) stunning result that there are classifiers that are universally consistent. Other topics include classification, plug-in methods (k-nearest neighbors), reject option, empirical risk minimization, Vapnik-Chervonenkis theory, fast rates via Mammen and Tsybakov's margin condition, convex majorizing loss functions, RKHS methods, support vector machines, lasso type estimators, low-rank multivariate response regression, random matrix theory, topic models, latent factor models, and interpolation methods in high dimensional statistics.

Prerequisites REF-FA25/Corequisites REF-FA25 basic mathematical statistics (STSCI 6730/MATH 6730 or equivalent) and measure theoretic probability (MATH 6710), or permission of instructor. Corequisites: None.

Enrollment Priority REF-FA25 Enrollment limited to: graduate students.

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