Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.
Covers local averaging predictors, including K-nearest neighbors and Nadaraya-Watson estimators, as well as local linear regression and its applications.
Discusses the evaluation of binary classifiers, including recall, sensitivity, specificity, ROC curves, and performance measures.
Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.
