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
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.
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/ 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.
Course Information |
Lecture Schedule/Notes |
CSC412/2506 - Uncertainty and Learning in Artificial Intelligence || www.cs.toronto.edu/~roweis/csc412/