Introduces linear regression basics from an empirical risk minimization perspective, covering the square loss, data preprocessing, and gradient computation.
Explores diverse regularization approaches, including the L0 quasi-norm and the Lasso method, discussing variable selection and efficient algorithms for optimization.
Covers local averaging predictors, including K-nearest neighbors and Nadaraya-Watson estimators, as well as local linear regression and its applications.
