Linear Models: Part 1

This lecture introduces linear models, starting with a recap on data attributes and insight into supervised and unsupervised learning algorithms. It covers simple parametric models like lines and planes, linear regression, derivatives, gradients, and hyperplanes. The lecture delves into minimizing risk, empirical risk minimization, and the transition from regression to classification. It also discusses multi-output linear regression, decision boundaries, and the evaluation metrics for regression and classification problems.