This course provides an introduction to probabilistic graphical modeling in the context of information retrieval. Starting with a review of basic concepts from statistics including notions of conditional independence and the maximum likelihood principle, the course will introduce the concepts of factorization of a probabilistic model on a graph, the properties of the such models and the associated semantics. The course will then introduce gradually new concepts and algorithms to perform inference and learning with graphical models through examples that are directly relevant to IR including the Naive Bayes model, probabilistic Latent Semantic Analysis, the Latent Dirichlet Allocation, and time varying models.