Statistical Learning

Catalog description

Algorithms for learning how to classify variables given a set of predictor variables. Linear regression, logistic regression and linear discriminant analysis. Cross-validation and bootstrapping. Model selection. Ridge regression and the LASSO. Nonlinear models, splines and generalized additive models. Tree-based methods, random forests and boosting. Support vector machines. Unsupervised learning methods are also discussed, including principal components and k-means clustering. (Offered in Spring)