STAT-627 Statistical Machine Learning (3)

Introduction to statistical concepts, models, and algorithms of machine learning. Explores supervised learning for regression and classification, unsupervised learning for clustering and principal components analysis, and related topics such as discriminant analysis, splines, lasso and other shrinkage methods, bootstrap, regression and classification trees, and support vector machines, along with their tuning, diagnostics, and performance evaluation. Includes review of linear algebra and optimization methods supporting the above topics. Crosslist: STAT-427. Grading: A-F only. Prerequisite: STAT-520 or STAT-615.