Session 1: Introduction, loss function, likelihood, and beyond

Session 2: Ridge, lasso, and lar

Session 3: Cross validation,and model selection

Session 4: Smoothing splines, kernel methods, and lowess

Session 5: Flexible discriminant, carts, mars, and random forest

Session 6: Support vector machines and Hilbert spaces

Session 7: Final exam, project presentation