Bayesian Networks: Fundamentals and Applications

This lecture introduces Bayesian Networks (BNs) as directed graphical models to represent joint distributions. It covers the basic equations for BNs, plate notation, types of variables, and key problems like learning model parameters. The lecture also explores probabilistic topic models, focusing on Latent Dirichlet Allocation (LDA) for unsupervised learning in text collections.