4.0

Average Course Rating

Rigorous mathematical analysis of statistical learning models, with an emphasis on underlying theory, along with integrated computation and applications. Brief review of background and an introduction to learning problems, followed by regression (linear, logistic, lasso, and kernel), support vector machines, clustering, principal component analysis, LDA, EM, and deep learning. Covers computational aspects, including optimization, approximation-generalization and bias-variance tradeoffs, model validation, and selection.