This course is designed to familiarize M.S. and Ph.D. students with a general knowledge about uncertainity, belief, probability, various distribution models, graphical models, directed and undirected probabilistic graphical models, inference and learning in such models. With this course, students will be able to design probabilistic graphical models for their own problems and provide inference and learning specifically for their problems. They will becaome to be able to provide some theoretical and practical improvements in such models.