The objective of this class is to provide an in-depth exposition of representation and reasoning under uncertainty using the framework of Graphical models. The class focuses on reasoning with uncertainty using directed and undirected graphical models such as Bayesian networks, Markov networks and constraint networks. These graphical models encode knowledge as probabilistic relations among variables. The primary reasoning tasks are, given some observations, to find the most likely scenario over a subset of propositions, or to update the degree of belief (distribution) over a subset of the variables. The primary algorithms (exact and approximate) using variational message-passing, search and sampling will be covered, with illustrations from areas such as bioinformatics, diagnosis and planning. Additional topics may include: causal networks, and dynamic decision networks (Influence diagrams and MDPS), as time permits.

Prerequisites

Course material

The course will be based mostly on four sources:

• [Dechter1] Rina Dechter, "Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms"
• [Darwiche] Adnan Darwiche, "Modeling and Reasoning with Bayesian Networks"
• [Dechter2] Rina Dechter, "Constraint Processing"
• [Pearl] Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference"

Additional sources:

• Russell and Norvig, Artificial Intelligence: A Modern Approach
• Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques

A longer list including secondary references.

Some links to software and tools.

Syllabus

Assignments:

Grading Policy: