CSC412S Spring 2006 - Info

*** LECTURES MW 2:10-3:00pm GB244 ***
*** TUTORIALS F 2:10pm GB244 ***

Course info sheets (ps)(pdf)

Instructor: Sam Roweis; email csc412 at cs.toronto.edu
Tutor: Ruslan Salakhutidnov; email csc412 at cs.toronto.edu

Please do NOT send Roweis or tutor email about the class directly to their personal accounts.
They are not able to answer class email except to cs412 at cs.toronto.edu.

Lecture Times: Mondays, Wednesdays 2:10pm -- 3:00pm
Lecture Location:GB244
First lecture Jan 9, last lecture April 7.
No lectures Feb 20/22 (Reading Week).

Tutorial Times: Fridays, 2:10pm-3:00pm
Tutorial Location: GB244
First tutorial Jan 13, last tutorial April 10.
No tutorial Feb 24 (Reading Week).

Office Hours: Wednesdays 11-12 or by appointment

Prerequisite: CSC411H (except in 2006); CGPA 3.0; but permission of instructor can waive these
Load: 26L, 13T

Textbook
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%
NO FINAL EXAM

Course Description

A senior undergraduate 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.

CSC412 - Probabilistic Learning and Reasoning || www.cs.toronto.edu/~roweis/csc412/