Fundamentals of Bayesian Networks (directed graphical models): representation, semantics, learning and inference [11 Lectures]
 Introduction to the class
 Representation, semantics
 MLE parameter learning
 Structure learning for BNs  complete data
 Constraintbased
 ChowLiu
 Fixedorder
 Structure search
 Exact inference
 Variable elimination
 Junction trees
 Contextspecific independence
Mon., Sept. 08:
 Introduction
[Slides] [Annotated]
 JavaBayes Applet
Wed., Sept. 10:
 NO CLASS
Mon., Sept. 15:
 BN Semantics
[Slides] [Annotated]
Wed., Sept. 17:
 BN Semantics 2  Representation Theorem, dSeparation
[Slides]
[Annotated]
Mon., Sept. 22:
 BN Semantics 3  dSeparation, minimal Imap, perfect maps
[Slides]
[Annotated]
Wed., Sept. 24:
 Perfect maps, Parameter Learning
[Slides]
[Annotated]
Mon., Sept. 29:
 NO CLASS
Wed., Oct. 1:
 Parameter Learning (MLE), Structure learning (The Good)
[Slides]
[Annotated]
Mon., Oct. 6:
 Bayesian Parameter Learning, Bayesian Structure learning
[Slides]
[Annotated]
Wed., Oct. 8:
 Structure learning: The Good, The Bad, The Ugly
A little inference too
[Slides]
[Annotated]
Mon., Oct. 13:
 Structure learning: The Good, The Bad, The Ugly (conclusion)
Inference
[Slides]
[Annotated]
Wed., Oct. 15:
 Variable Elimination
[Slides]
[Annotated]
Mon., Oct. 20:
 Variable Elimination Complexity, MPE Inference, Junction Trees
[Slides]
[Annotated]
Wed., Oct. 22:
 Junction Trees 2
[Slides]
[Top]
Representation revisited [3 Lectures]
 Undirected models, Markov Random Fields
 Factor graphs  unifying representation
 Exponential family
Mon., Oct. 27:
 Junction Trees 3, Undirected Graphical Models
[Slides]
[Annotated]
Wed., Oct. 29:
 Undirected Graphical Models
[Slides]
[Annotated]
[Top]
Inference revisited:approximate inference [3 Lectures]
 Sampling
 importance sampling
 MCMC, Gibbs
 Variational inference
 Loopy belief propagation
 Generalized belief propagation
 Kikuchi
Mon., Nov. 3:
 Undirected Graphical Models, Variational Inference
[Slides]
[Annotated]
Wed., Nov. 5:
 Variational Inference, Loopy BP
[Slides]
[Annotated]
Mon., Nov. 10:
 Sampling
[Slides]
Wed., Nov. 12:
 Generalized Belief Propagation
Parameter learning in Markov networks
[Slides]
[Annotated]
[Top]
Learning revisited [3 Lectures]
 Parameter estimation in BNs with missing data
 EM
 Gradient descent
 Structure learning for BNs  missing data
 Learning undirected graphical models
 Gradient algorithms
 IPF for tabular MRFs
 Structure Learning
Mon., Nov. 17:

Parameter learning in Markov networks
Conditional random fields
EM
[Slides]
[Annotated]
Mon., Nov. 24:

EM for BNs
Gaussians
[Slides]
[Annotated]
[Top]
Special graphical models / Applications [6 Lectures]
 Gaussian
 Multivariate Gaussians
 Gaussian graphical models
 Inference in Gaussian models
 Hybrid models
 discrete and continuous variables
 Hidden Markov Models
 Representation
 Inference
 ForwardsBackwards
 Viterbi
 BaumWelch
 Kalman filter
 representation
 linearization
 switching Kalman filter
 assumed density filtering
 DBNs
 Representation
 Inference
 BK
Mon., Dec. 1:

Gaussians, Kalman Filters, Gaussian MNs
[Slides]
[Annotated]
[Top]
Advanced Topics [3 Lectures]
 Causality
 Relational probabilistic models
 Template models
Mon., Dec. 3:

DBNs, Overview
[Slides]
[Annotated]
[Top]
Project Poster Session
36pm, Monday, Dec 1st
NSH Atrium
[Top]
Project Paper Due
Wednesday, Dec. 3rd by 3pm by email to the instructors list
[Top]
Final Exam
[Top]