Linear Models for Classification

This lecture covers linear models for classification, starting with the basics of parametric models like lines and hyperplanes, moving on to linear regression, binary classification, and logistic regression. It also delves into model evaluation metrics, the concept of margin between classes, and the formulation for a maximum margin classifier. The lecture introduces the notion of constrained optimization and discusses the challenges of overlapping classes in practical scenarios.