Probabilistic Graphical Models

EE 639 Advanced Topics in Signal Processing and Communication

Fall 2014 


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[Home] [Lectures] [Final Project]

Tentative Lecture Schedule

Note: Most of the lecture notes are based on the original slides provided by Professor David Barber.

Lecture

Topics

Readings

Other Material

Homework

Week 1

Overview

 

 

HW1 (9/10)

Week 1

Probabilistic Reasoning

Ch. 1

 

Week 2

Graph Theory, Belief Network

Ch. 2 & 3

 

HW2 (9/26)

More on Belief Network

Week 3

Markov Networks

Ch. 4

Proof of Hammersley-Clifford Theorem

Week 4

Inference

Ch. 5

 

HW3 (10/17)

Week 5

Junction Tree

Ch. 6

Equivalence between JT and Triangulation

Week 6

Review & Midterm 1

 

 

 

Week 7

Statistical Concept

Ch. 8

 

HW4 (11/21)

Week 8

Parameter Learning

Ch. 9.1-4, 9.6

 

Week 9

Structure Learning

Ch. 9.5, Ch. 12.1

 

Week 10

Learning with Hidden Variables

Ch. 11, Ch. 20.1-3

 

Week 10

Machine Learning Concept

Ch. 13

 

Week 11

Linear Models

Ch. 17, 18

 

Week 14

Review & Midterm 2

 

 

 

Week 16

Final Project Presentation