Kernel Methods: Machine Learning

This lecture covers Kernel Methods in Machine Learning, focusing on topics such as overfitting vs underfitting, model selection, validation set method, LOOCV, k-fold cross-validation, penalizing overfitting, regularized linear regression, polynomial feature expansion, kernel functions, Gaussian kernel, polynomial kernel, feature expansion properties, kernel trick, kernel regression, Representer theorem, multi-output linear regression, kernelization, SVM dual formulation, prediction, kernel SVM, and kernel methods advantages and drawbacks.