Linear Regression: Regularization Overview

This lecture covers the fundamentals of linear regression, focusing on the importance of regularization techniques such as L2 regularization. It explains the concept of probabilistic models, the generation of labels, and the need for regularization to prevent overfitting and improve model performance. The instructor discusses the process of predicting new values and estimating parameters, emphasizing the significance of statistical inference and the Bayesian approach. The lecture also delves into the relationship between linear regression and inverse problems in signal processing, highlighting the role of regularization in optimizing model accuracy.