Probabilistic Linear Regression

This lecture covers the probabilistic interpretation of linear regression, including joint probability, conditional probability, and the use of ridge regression. It also discusses the notion of overfitting, ways to quantify it, and methods to mitigate it, such as cross-validation. The instructor explains the importance of statistical inference in estimating ground truth parameters and the posterior distribution. Various concepts like marginal probability, pseudoinverse, and Bayesian rule are explored in the context of linear regression.