In this graduate-level seminar, we will focus on graphical models, which are a popular tool to represent the dependence relations among random variables. In the first part of the semester, we will review directed and undirected graphical models, Markov properties, Gaussian graphical models, and frequentist and Bayesian methods graphical model inference. Later in the semester, we will discuss recent research in graphical models.