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 205 and STAT 206B; or STAT 205B. Enrollment is restricted to graduate students.

5 credits

Year Fall Winter Spring Summer

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