Supervised Learning: Regression Methods

This lecture covers supervised learning focusing on regression methods. It starts with linear regression, model fitting, and numerical issues. Then, it delves into model selection, performance evaluation, and regularization techniques like ridge regression and Lasso. The lecture also discusses subset selection, cross-validation, and the bias-variance trade-off. Additionally, it explores regression trees, random forests, boosting, and their applications in predictive modeling.