A. Laio and M.S. Bernardi, 18 hours

• Introduction to Statistical Learning

• Dimension reduction: Principal Components Analysis

• Linear Models: Simple and multiple linear regression

• Linear Model Selection and Regularization: Best Subset Selection, Stepwise Selection, Shrinkage Methods (Ridge Regression and Lasso)

• Supervised classification: Logistic Regression, Linear and Quadratic Discriminant Analysis

• Unsupervised classification: Hierarchical clustering and K-means clustering

• Resampling Methods: Cross-Validation and Bootstrap

• Tree-based Methods: Classification and Regression trees