Kernel Methods: SVM and Regression

This lecture covers the concepts of linear models, margin, maximum margin classifier, support vector machine, slack variables, curse of dimensionality, nearest neighbor method, k-nearest neighbors, polynomial curve fitting, polynomial feature expansion, kernel functions, kernel trick, kernel regression, Gaussian process regression, and the dual formulation of SVM. It also explains the importance of feature expansion, the Representer theorem, and the prediction process in kernel regression. The lecture concludes with examples of kernel SVM and the advantages and drawbacks of kernel methods.