MATH 7740

Statistical Learning Theory

Course Description

Course information provided by the Courses of Study 2023-2024. Courses of Study 2024-2025 is scheduled to publish mid-June.

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 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. Further, active high-dimensional statistical research topics such as lasso type estimators, low-rank multivariate response regression, topic models, latent factor models, and interpolation methods are presented.

When Offered Fall.

Permission Note Enrollment limited to: graduate students.
Prerequisites/Corequisites Prerequisite: basic mathematical statistics (STSCI 6730/MATH 6730 or equivalent) and measure theoretic probability (MATH 6710).

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