Introduction to Statistical Machine Learning
Introduces the theory and application of statistical machine learning. Topics may include supervised versus unsupervised learning; cross-validation; the bias-variance trade-off; regression and classification; regularization and shrinkage approaches; non-linear approaches; tree-based methods; and support vector machines. Includes applications in R. Prerequisite: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 208. Offered: Sp.