STAT227: Statistical Learning and High Dimensional Data Analysis

Introductions to statistical learning, modeling, and inference with complex, large, and high-dimensional data. Topics include supervised and unsupervised learning, model selection, dimension reduction, matrix factorization, latent variable models, graphical models, interpretability and causality. Applications in health, social sciences, and engineering. Prerequisite(s): STAT 207. STAT 205 recommended. Enrollment restricted to graduate students.

5 Credits


While the information on this web site is usually the most up to date, in the event of a discrepancy please contact your adviser to confirm which information is correct.