Derivation of Ridge and Lasso Regularization from Bayesian Principles

In this lesson, we start from the Bayes rule and show how it reduces to a point estimation of parameters when maximized the log value of the posteior distribution. Finally we show how to get Ridge regularization through a Gaussian prior and Lasso regularization through a Laplace prior.