CSC412S Spring 2002 - Info


Instructor: Sam Roweis; email
Tutor: Yee-Whye Teh; email

Please do NOT send Roweis or Teh email about the class directly to their personal accounts.
They are not able to answer class email except to

Lecture Times: Mondays, Wednesdays 10:10am -- 11:00 am
Lecture Location:Pratt 266 (NOTE: Room has been changed from LM158)
First lecture Jan7, last lecture April 10.
No lectures Feb 18/22 (Reading Week).

Tutorial Times: Fridays, 10:10am-11:00am
Tutorial Location: Pratt 266 (NOTE: Room has been changed from SS1080)
First tutorial Jan 11, last tutorial April 12.
No tutorials Feb 24 (Reading Week) or March29 (Good Friday).

Office Hours: by appointment

Prerequisite: CSC384H, 411H; CGPA 3.0; Load: 26L, 13T

Michael Jordan, An Introduction to 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 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. Application of these models in other areas of artificial intelligence and to problems such as medical diagnosis.

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CSC412 - Uncertainty and Learning in Artificial Intelligence ||