Kernel Methods

This lecture covers the concepts of overfitting vs underfitting, model selection, validation set method, LOOCV, k-fold cross-validation, overfitting with a linear model, penalizing overfitting, regularized linear regression, kernel functions, kernel trick, kernelized versions of linear regression and SVM. It also introduces the notion of kernel function, kernelized linear and ridge regression, and the Gaussian kernel. The lecture explains kernel regression, kernel ridge regression, multi-output linear regression, kernel SVM, prediction in kernel regression and SVM, and provides examples of kernel regression and SVM. It concludes with a discussion on kernelization of algorithms like SVM and dimensionality reduction.