Classification: Basics and Techniques

This lecture covers the fundamentals of classification in machine learning, focusing on predicting labels for new observations. Topics include binary and multi-class classification, decision boundaries, credit card default prediction, sensitivity to unbalanced data, and the distinction between classification and regression. Various classification approaches such as Nearest Neighbor, k-Nearest Neighbor, linear decision boundaries, and support vector machines are discussed. The importance of margin in separating hyperplanes and the concept of the Bayes classifier are also explained.