CSC412S Spring 2006  Lectures
Tentative Lecture Schedule
 Jan 9  Uncertainty in AI, Basic Learning Problems
(notes [ps.gz]
[pdf])
 Jan 11  Probabilistic Graphical Models, Bayes Ball Algorithm
(notes [ps.gz]
[pdf])
 Jan 13  Tutorial: Probability and Statistics Review
(notes [ps.gz]
[pdf])
 Jan 16  Undirected Graphical Models
(notes [ps.gz]
[pdf])
 Jan 18  CPTs, Gaussian and Exponential Distributions
(notes [ps.gz]
[pdf])
 Jan 20  Tutorial: Assignment#1 questions
 Jan 23  Statistical Parameter Estimation:
Basic Models, Directed Graphs, Linear Regression
(notes [ps.gz]
[pdf])
 Jan 25  Classification Models
(notes [ps.gz]
[pdf])
 Jan 27  NO TUTORIAL
 Jan 30  Classification Models (again)
(notes [ps.gz]
[pdf])
 Feb1  Tree Structured Models
(notes [ps.gz]
[pdf])
 Feb 3  Tutorial: Assignment#2 questions
 Feb 6  Latent Variables, Missing Data, Mixture Models/Density, Mixtures of Experts/Conditional
(notes [ps.gz]
[pdf])
 Feb 8  EM Algorithm
(notes [ps.gz]
[pdf])
 Feb 10  Tutorial: Midterm Review
Example questions for the midterm are available in
[ps] or [pdf].
 Feb 13  Factor Analysis and PCA
(notes [ps.gz]
[pdf])
 Feb 15  MIDTERM TEST 2pm sharp  3pm
 Feb 17  (lecture during tutorial time) Iterative Proportional Fitting
(notes [ps.gz]
[pdf])
 Feb 2024  READING WEEK  no classes/tutorials
 Feb 27  Bayesian Statistics, Plates
(notes [ps.gz]
[pdf])
 Mar 1  Inference: Node Elimination
(notes [ps.gz]
[pdf])
 Mar 3  No Tutorial this week
 Mar 6  Belief Propagation on Trees
(notes [ps.gz]
[pdf])
 Mar 8  Markov and Hidden Markov Models,
Dynamic Programming and Shortest Paths
(notes [ps.gz]
[pdf])
 Mar 10  Tutorial: Assignment#3 questions
 Mar 13  HMM Inference and Learning
(notes [ps.gz]
[pdf])
 Mar 15  A3 due today in class
 Mar 15  Inference in Profile HMMs
(notes [ps.gz]
[pdf])
 Mar 17  No Tutorial
 Mar 17  A4 posted
 Mar 20  Junction Trees: Clique Trees,
Moralization, Potential Initialization
(notes [ps.gz]
[pdf])
 Mar 22  Junction Trees: Triangulation, Junction Tree Construction
(notes [ps.gz]
[pdf])
 Mar 24  Tutorial: A4 questions
 Mar 27  Junction Trees: Final Hugin/SS Algorithms
(notes [ps.gz]
[pdf])
 Mar 29  Junction Tree Derivation of HMM Inference
(notes [ps.gz]
[pdf])
 Mar 29  Factor Graphs
(notes [ps.gz]
[pdf])
 Mar 31  No tutorial today.
 Mar 31  A4 due today by 2pm in Pratt 283.
 April 3  Features and Maximum Entropy Models
(notes [ps.gz]
[pdf])
 April 5  Iterative Scaling
(notes [ps.gz]
[pdf])
 April 7  Tutorial: final test questions/review
 April 10  Brief Applications:
Quick Medical Reference, Bioinformatics
Web Document Classification, Information Retrieval
(notes coming soon)
 April 12  FINAL TEST
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CSC412  Probabilistic Learning and Reasoning  www.cs.toronto.edu/~roweis/csc412/
