Introduction to Machine Learning: Linear Models

This lecture covers the basics of supervised learning, focusing on linear models such as linear regression and linear classification. It explains the concepts of overfitting, regularization, and the use of kernels to handle non-linear data. The instructor discusses the application of linear models in machine learning tasks and introduces the concepts of k-Nearest Neighbors, Principal Component Analysis (PCA), and Fisher Linear Discriminant Analysis (LDA) for dimensionality reduction and classification.