# A Probabilistic Interpretation of Regularization

This post is going to look at a probabilistic (Bayesian) interpretation of regularization. We'll take a look at both L1 and L2 regularization in the context of ordinary linear regression. The discussion will start off with a quick introduction to regularization, followed by a back-to-basics explanation starting with the maximum likelihood estimate (MLE), then on to the maximum a posteriori estimate (MAP), and finally playing around with priors to end up with L1 and L2 regularization.