Cross-validation & Regularization

This lecture covers the concepts of polynomial curve fitting, feature expansion, kernel functions, and regularization techniques such as ridge regression. It also delves into the importance of model complexity, overfitting, and the use of cross-validation methods like k-fold and leave-one-out. The instructor explains how to handle nonlinear data using kernel methods and discusses the application of regularization in logistic regression and support vector machines.