The importance of relational (structured) data is evident from its increasing presence: WWW, social networks, relational databases, bibliographic networks, organizational networks, biological pathways, and many more. The rich information in relational data gives rise to a wealth of potential patterns that may characterize a network. The ability to describe and detect relational patterns provides powerful support for many applications, including social network analysis, viral marketing, information extraction, drug discovery, computer vision, robotics and many more. In this workshop, we will explore Statistical Relational Learning (SRL) methods that extend machine learning techniques so that they apply to relational domains made up of objects that interrelate. SRL systems employ probability to reason about uncertainty in network structures. They utilize the expressive power of formal logic to represent the full complexity of heterogeneous networks with multiple types of links, nodes, and attributes. In addition to learning about the different formalisms, we will also cover learning and inference algorithms for such models. The workshop will be a 2 day one with the first day focusing on fundamentals of probabilistic graphical models and the second day focusing on the SRL models. Basic knowledge of AI and ML is encouraged. Elements of probability theory (Bayes rule, axioms of probability etc) is required.