CS539: Probabilistic Relational Models

Course Description

Machine Learning is in the midst of a revolution. The "old" approach to machine learning focused on supervised learning from independent and identically distributed (iid) training examples. The goal was to learn a classifier f that given an object x would produce as output a classification label y = f(x).

The "new" approach focuses on learning a complex web of relationships among a collection of diverse objects. Examples include diagnosing the disease of a patient based not only on properties of that patient but also on properties of other people that patient lives with or has had contact with. A new formalism has been developed called Probabilistic Relational Models (PRMs) that can represent these webs of relationships and support learning and reasoning with them.

This course will provide an introduction to PRMs for graduate students interested in doing research in this area. The course will begin with a rapid review of bayesian networks and Markov random fields including representation and inference. Then we will read and discuss all of the papers published on Probabilistic Relational Models (PRMs) and Relational Markov Networks (RMNs). Students will make class presentations, develop PRMs and RMNs for various application problems, and identify problems for future research. The class will involve substantial work outside of class including a class project.

Prerequisites: Consent of the instructor; basic knowledge of probability

Registration Information: 1-4 Units. TTh 2:00-2:50pm Strand 323

Course Handouts

Viewgraphs for Lectures

Software Resources

Reading Schedule

Programming Tasks

Program Task Assignments (Dan Vega's page).

I'm listing here some tasks that I believe will require additional research (including searching to see what has already been done and possibly the development of new techniques).

Reading List

We will reading the following papers:

Other Resources

Tom Dietterich, tgd@cs.orst.edu