Linear Regression: Basics and Applications

This lecture introduces the basics of linear regression in machine learning, covering topics such as supervised learning, empirical risk, loss functions, and least-squares minimization. It explores how linear regression can be applied to predict outcomes like birth weight based on various attributes. The lecture also delves into the concepts of correlation and regression, showcasing how scatterplots and least-squares regression lines can be used to analyze relationships between variables.