Statistical Machine Learning

Catalog description

This course will cover the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include Bayesian, maximizing a posteriori (MAP), and maximum likelihood (ML) parameter estimation, regularization and sparsity-promoting priors, kernel methods, adaptive basis function methods, the expectation maximization algorithm, Monte Carlo methods, variational methods, and models for data with temporal or hierarchical structure. Applications to regression, categorization, clustering, and dimensionality reduction problems are illustrated by examples. Each student will complete several problem sets, including both mathematical and computer implementation problems. Probability and Statistics I, Linear Algebra, and Introduction to Computer Programming. Familiarity with a numerical mathematics package (e.g. Matlab, Maple, Mathematica) is helpful but not required.