Introduction
Given data and a model family, which parameters are best? Maximum likelihood estimation (MLE) answers: choose the parameters under which the observed data are most probable. Nearly every standard loss function — mean squared error, cross-entropy — is a negative log-likelihood in disguise, so MLE is how most models are trained.