Regularization in Machine Learning

This lecture covers the concepts of overfitting, regularization, and cross-validation in machine learning. It explains how to handle nonlinear data, polynomial curve fitting, feature expansion, kernel functions, and kernelized algorithms. The instructor discusses the importance of model complexity, k-Nearest Neighbors, and the impact of different k values on training and test errors. The lecture also delves into cross-validation methods like leave-one-out and k-fold, highlighting their advantages and drawbacks. It concludes with demonstrations on ridge regression, kernel ridge regression, and the incorporation of regularization in logistic regression and support vector machines.