IDS/ACM/CS 158: Fundamentals of Statistical Learning

The main goal of the course is to provide an introduction to the central concepts and core methods of statistical learning, an interdisciplinary field at the intersection of statistics, machine learning, information and data sciences. The course focuses on the mathematics and statistics of methods developed for learning from data. Students will learn what methods for statistical learning exist, how and why they work (not just what tasks they solve and in what built-in functions they are implemented), and when they are expected to perform poorly. The course is oriented for upper level undergraduate students in IDS, ACM, and CS and graduate students from other disciplines who have sufficient background in probability, statistics, and linear algebra. Topics covered include statistical decision theory, regression and classification problems, classical linear regression, subset selection, shrinkage methods, ridge regression and lasso, cross-validation, logistic regression, linear and quadratic discriminant analysis, tree-based methods, and support-vector machines.