Mathematics of Statistical Learning

Catalog description

Learning theory is an important branch of modern statistics. This course gives an overview of various topics and proof techniques that include concentration inequalities, Bayes rules, reject option, margin condition, local averaging methods, universal consistency, empirical risk minimization, convex surrogate losses, Rademacher complexity, VC theory, structural risk minimization, sparse methods, low-rank regression, topic models, latent factor models and interpolation methods.