Instructor: Zaid Harchaoui
Introduces the theory and application of statistical machine learning. Topics include supervised versus unsupervised learning; cross-validation; the bias-variance trade-off; classification; k-means and hierarchical clustering; regularization and shrinkage approaches; non-linear approaches; local regression, spline models and generalized additive models; tree-based methods; and support vector machines.