CSC412S Spring 2002 - Info
*** LECTURES AND TUTORIALS MOVED TO PRATT 266 ***
Instructor: Sam Roweis; email email@example.com
Tutor: Yee-Whye Teh; email firstname.lastname@example.org
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 email@example.com.
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.
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
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.
Course Information |
Lecture Schedule/Notes |
Free Marks |
CSC412 - Uncertainty and Learning in Artificial Intelligence || www.cs.toronto.edu/~roweis/csc412/