CMPUT 651 - Probabilistic Graphical Models

Overview

In the past decade, probability models have revolutionized several areas of artificial intelligence research, including expert systems, computer perception (vision and speech), natural language interpretation, automated decision making, and robotics. In each of these areas, the fundamental challenge is to draw plausible interpretations from inputs that are uncertain and noisy. As a model of uncertainty, probability models are unparalleled in their ability to combine heterogeneous sources of evidence effectively. However, until recently, the use of probability models has been limited by the inherent complexity of realizing exact probabilistic inference. Now, recent advances from computing science have made many probabilistic inference tasks, practical. This has led to the field of Probabilistic Graphical Models (PGMs), which includes Bayesian belief nets, Markov random fields, and other representations.

This course provides an introduction to the field, describing semantics, inference, and learning, as well as practical applications of these systems. It will cover the fundamentals of graphical probability models, focusing on the key representations, algorithms, and theories that have facilitated much recent progress in artificial intelligence research. Programming assignments will include hands-on experiments with various reasoning systems.

Objectives

• Understand the foundations of factored distributions
• Understand when to use each type of PGM model
• Have experience using various software tools for PGM inference and for learning PGMs

Course Work

• Assignments (coding and companion problem sets)
• Course participation
• Team project (2 presentations, and written report)