Linear Regression: Basics

This lecture covers the basics of linear regression, starting with the definition of data attributes and the insight into supervised learning algorithms. It then delves into the solutions and applications of linear regression, including the concepts of parametric models, planes, and hyperplanes. The lecture also explores the gradient, matrix form, and test time predictions in linear regression. Additionally, it introduces the challenges faced in binary classification and the transition to multi-class classification. The lecture concludes with an overview of evaluation metrics in regression and classification, such as mean squared error, confusion matrices, accuracy, precision, recall, false positive rate, F1 score, and ROC curve.