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 of 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 course 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.
There are no formal prerequisites for this course---all that is required is a basic programming capability and a rudimentary knowledge of probability and statistics. It would be advantageous (but not essential) to have some prior exposure to optimization methods, algorithms and complexity, and a previous course on artificial intelligence.
Time: Monday/Wednesday 4:10 pm -5:25 pm; Location: Oelman 309
387, Joshi Center
Office hours: Tuesday/Thursday 2:30PM-4:00PM
Steffen L. Lauritzen
Oxford University Press , 1996.
Probabilistic Reasoning in Intelligent Systems
Morgan Kaufmann, 1988.
Daphne Koller and Nir Friedman
Probabilistic Graphical Models: Principles and Techniques
MIT Press, 2009.
Course Grades and Workload
Two homeworks 45%
Attendance and project presentation 10%
Probability and Statistics
Programming language: matlab, C++, Java