We will discuss probabilistic graphical models associated to directed and undirected graphs. We will introduce exact inference algorithms, such as the sum-product algorithm, and apply it to hidden Markov models. We will also discuss elements of learning in graphical models including maximum likelihood, maximum a posteriori and the expectation-maximisation algorithm.