Lecture, three hours. Recommended requisite: course 200A. Introduction to graphical models with applications in statistical modeling, machine learning, and causal inference. Common graphical models, such as undirected graphs, directed acyclic graphs, and ancestral graphs, for modeling conditional independence and causality. Methods and theory for structure learning of graphical models from observational and experimental data. S/U or letter grading.