In this tutorial, we will see why it is important to have a probabilistic first view when modeling. This view enables us to have predictive distributions for our target variables instead of focusing on point estimate values.

We will also see that linear regression is a special case of maximum likelihood estimation when Gaussian noise is assumed. The sum squared error function formulation from part 1 will also naturally arise in this framework. In other words, no justification is needed anymore.