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 course, we will explore Statistical Relational AI (StaRAI) methods that extend machine learning techniques so that they apply to relational domains made up of objects that interrelate. StaRAI 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


The course gives an introduction to statistical relational artificial intelligence. The course will be a combination of lectures and presentations from students on different topics. We expect the students to dive deep down into the current trends in STARAI literature such as probabilistic programming, relational models such as Markov Logic Networks to name a few and work on a project either applying these methods to real world problems or develop new STARAI methods


Math classes from the bachelor's degree, basic programming abilities, introductory classes to computer science.


The most important book for this class are:

  1. De Raedt, Kersting, Natarajan and Poole. Statistical Relational Artificial Intelligence, Morgan and Claypool free online copy

Additionally, the following book might be useful for specific topics:

  1. L. Getoor and B. Taskar. Introduction to Statistical Relational Learning, MIT Press Free online copy


  • The homework will be given during the course.
  • Students are expected to read papers and present and discuss them during the class.
  • There will be a final exam. 
  • Details will be announced during the class.

The course website can be found at
All participants can check the zoom meeting code in the announcements.