Feature Expansion and Kernel Methods

This lecture covers the concepts of feature expansion and kernel methods in machine learning. It starts with a recap of linear models, margin, maximum margin classifier, and support vector machines. It then delves into topics like overlapping classes, slack variables, different linear models for classification, and the impact of loss functions. The lecture progresses to discuss polynomial curve fitting, nearest neighbor methods, k-nearest neighbors, curse of dimensionality, and nonlinear classification. It concludes with an in-depth exploration of polynomial feature expansion, kernel functions, kernel regression, kernel SVM, and the kernel trick.