Probabilistic Graphical Models and Maximum Likelihood Estimation - Lecture 13

This lecture introduces network structure and graph representations for modeling dependencies in data. It covers exponential and Gaussian likelihoods, maximum likelihood estimation, Bayes rule, and conditional independence, including linear Gaussian models.