Statistics

STAT 227 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.

Requirements

Prerequisite(s): STAT 205 and STAT 206B; or STAT 205B. Enrollment is restricted to graduate students.

Credits

5