What is regularization from a Bayesian perspective?

Regularization corresponds to placing priors on model coefficients.

Ridge regression is equivalent to Gaussian prior on coefficients, while Lasso corresponds to Laplace prior. The regularization term acts as log-prior in MAP estimation, blending likelihood with prior belief.