# Q18 – Regularization

Show that L2 regularization applied to a linear regression with weights $\boldsymbol{w}$, input data $\boldsymbol{x}$ and targets $\boldsymbol{y}$ with mean squared error loss function corresponds to assuming a Gaussian prior over the weights.

## 3 thoughts on “Q18 – Regularization”

1. And inspire also from formula (7.2) that shows that L1 corresponds to a Laplacian prior over weights

Like