stanford-stat-learning

Supervised learning in R, with a focus on regression and classification methods. Will be adding the following in Summer 2020: logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; and some unsupervisied learning.... principal components and clustering (k-means and hierarchical).