This seminar studies statistical representations of relational data, including Markov logic networks, relational Bayesian networks, and probabilistic databases. We will discuss their probabilistic inference problems, parameter, and structure learning algorithms, and representation power. The course also covers more general probabilistic programming languages and systems, which allow for more expressive statistical modeling.
This course requires advanced doctorate-level knowledge of artificial intelligence, machine learning, and probabilistic graphical models in particular.
Grading will be based on Attendance and Participation (20%), Paper Presentation (35%) and Project (45%).
The course will consist of instructor-led lectures on the foundational techniques, followed by student presentations of research papers, and project presentations.
This course will have student presentations on various research directions in SRL, spanning multiple papers. Students will also be required to do a short workshop-quality research project on SRL. You can work alone or in groups of two. For suggested topics, see CCLE.