Linear Regression Basics: ERM Perspective

This lecture covers the basics of linear regression from the empirical risk minimization perspective, focusing on the square loss. Topics include the design matrix, output vector, data preprocessing, ordinary least squares regression, and the minimization of empirical risk. The instructor explains how to compute gradients, the normal equations, and the conditions for a unique solution in linear regression.