Nearest Neighbor Classifiers and Curse of Dimensionality

This lecture covers the concept of nearest neighbor classifiers in supervised machine learning, focusing on predicting labels based on the closest data points. It discusses the function of nearest neighbors, their application in regression and classification tasks, and the bias-variance tradeoff. Additionally, it explores the curse of dimensionality, explaining how high-dimensional data affects the coverage of training sets and the distance between data points. The lecture also delves into generalization bounds for 1-NN, providing proofs and insights on error bounds and geometric terms.