Statistical Relational Learning concerns applications where relationships between objects are important. Examples are social networks (Jack, likes, Mary), a clinical setting (Jack, hasDisease, Diabetes) and knowledge graphs (Obama, presidentOf, USA). I will first review multivariate statistical models (Bayesian networks, Markov networks, mixture models, factor models) and then discuss how they are generalized to relational domains as probabilistic relational models, Markov logic networks, infinite hidden relational models and the RESCAL model.