The course is structured in three modules, dealing with graphical models, algorithms for doing inference in and learning of them, and the combination of logical and probabilistic approaches to reasoning.
Topics to be discussed include probabilistic graphical models, causal models, interventional distributions and structural learning algorithms (module 1); Markov chain Monte Carlo, approximate message-passing, and variational inference, with a particular emphasis on inference in probabilistic graphical models (module 2); Generalized syntax and semantics of propositional and (especially) predicate logic, as well as major results about algorithmic decidability and efficiency for logical formalisms (module 3).