Machine Learning Fundamentals: Overfitting and Regularization

This lecture covers the concepts of overfitting, regularization, and cross-validation in machine learning. It explains how to handle nonlinear data using polynomial curve fitting and feature expansion. The importance of mapping to a higher-dimensional space is discussed, along with kernel functions and the Representer theorem. The lecture also explores k-Nearest Neighbors, model complexity, and model selection through cross-validation methods.