Topics in Artificial Intelligence:
Probabilistic Graphical Models

Computing Science 651
September to December 2008

Time: MF 9:10 - 10:30am
Place: CSC B41
Intructors: Russ Greiner and Matt Brown

LectureNotes Eval'n (HWs Project )
GradeBook 		Textbooks
(Errata)		 Moodle page
Resources

Please answer the Mid-course survey... and bring it in on Monday 27/Oct.

Purpose

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 course provides a graduate-level introduction to the field, covering both inference and learning, as well as practical applications of these system. 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.

See Lecture Notes for more details.

Prerequisite

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, as well as linear algebra. It would be advantageous (but not essential) to have some prior exposure to optimization methods, statistics, and a previous course on artificial intelligence (eg, CMPUT 366).

We do assume that all students will know C/C++/JAVA, and know, or at least be willing to learn, Matlab.

Textbooks

Lecture Notes

Evaluation:

The final grade will be based on 3 assignments (20% each) and a project (40%).

              Activity % (Points)
       Due date
HW#1         pdf   (zipped tar) 20 (125)

 Friday, Oct. 10
(11:59pm)
P1         Project Proposals due Part of 40 Friday, Oct. 10
(5pm)
HW#2
(C2)
         Homework#2 20 (120)
 Friday, Nov. 14
(11:59pm)
P2         "Lay of the Land" presentations Part of 40 Friday, Oct. 31
Monday, Nov. 3
HW3
(C3)
         Homework#3 20 (65)
 Friday, Dec. 5
(11:59pm)
P3         Final presentations Part of 40 Friday, Nov. 28
Monday, Dec. 1
P4        Project write-up Part of 40 Monday, Dec. 15
(5pm)

Office Hours:

Russ Greiner Matt Brown
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