CSC412S/2506S Spring 2004 - Info

*** LECTURES M10 (GB404), W10 (GB304), TUTORIAL F10 (LP378) ***

Course info sheets (ps)(pdf)

Instructor: Sam Roweis; email csc412 at cs dot toronto dot edu
Tutor: Ben Marlin; email csc412 at cs dot toronto dot edu

Please do NOT send Roweis or Marlin email about the class directly to their personal accounts.
They are not able to answer class email except to csc412 at cs dot toronto dot edu.

Lecture Times: Mondays, Wednesdays 10:10am -- 11:00 am
Lecture Location:GB404/GB304
First lecture Jan5, last lecture April 7.
No lectures Feb 16/18 (Reading Week).

Tutorial Times: Fridays, 10:10am-11:00am
Tutorial Location: Pratt 378
First tutorial Jan 9, last tutorial April 7.
No tutorial Feb 20 (Reading Week).

Office Hours: Wednesdays 11-12 after class

Prerequisite: CSC384H, 411H; CGPA 3.0; but permission of instructor can waive these
Load: 26L, 13T

Michael Jordan, An Introduction to Probabilistic Graphical Models
This textbook is not yet published, but drafts will be provided in class.

Marking Scheme
2 small assignments worth 10% each
2 larger assignments worth 15% each
1 midterm test worth 25%
1 final test worth 25%

Course Description

A senior undergraduate/ first year graduate class on graphical models and probabilistic networks in AI.

Representing uncertain knowledge using probability and other formalisms. Qualitative and quantitative specification of probability distributions using graphical models. Algorithms for inference with graphical models. Statistical approaches and algorithms for learning models from experience. Examples will be given of applications of these models in various areas of artificial intelligence.

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CSC412/2506 - Uncertainty and Learning in Artificial Intelligence ||