Classification: Fundamentals and Algorithms

This lecture covers the fundamentals of classification in machine learning, starting with the definition and goal of classification. It explains binary and multi-class classification, linear decision boundaries, k-Nearest Neighbor algorithm, and the concept of margin in separating hyperplanes. The lecture also delves into the Bayes classifier, empirical risk minimization, and the importance of loss functions in classification. Additionally, it discusses the relationship between regression and classification, the impact of over-parameterization on loss functions, and different types of losses for classification tasks.