Linear Regression: Basics and Applications

This lecture introduces the basics of linear regression, focusing on 1D linear regression and its application in machine learning. It covers topics such as line fitting, training, minimizing risk, derivatives, gradients, and multivariate functions. The lecture also explores the transition from regression to classification, showcasing binary classification as a regression problem. Real-world examples, including wine quality prediction and author age prediction from text, are used to illustrate the concepts.