Probabilistic Graphical Models

EE 639 Advanced Topics in Signal Processing and Communication

Fall 2013 


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[Home] [Lectures] [Final Project Challenge]

Tentative Lecture Schedule

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

Day

Offline Lecture

Video

Readings

Homework

8/28

Overview and Background Mathematics I

Ch. 29.1

Homework 1

(due 9/9)

8/30

Background Mathematics II

Ch. 29.1

9/4

Optimization

Ch. 29.2-29.5.2

9/6

Probabilistic Reasoning

Ch. 1

9/9

Graph Theory and Belief Networks

Ch. 2 & 3.1-2

Homework 2

(due 9/18)

9/11

Belief Networks

Ch. 3

9/13

Markov Networks

Ch. 4

H-C Proof

 

 

9/16

Chain Graphs & Factor Graphs

Ch. 4

Homework 3

(due 9/30)

9/18

Efficient Inference on trees

Ch. 5

9/20

Other Inference

Ch. 5

9/23

Review

9/25

Review

 

 

9/27

Junction Tree Algorithm I

Ch. 6

Homework 4

(due 10/7)

9/30

Junction Tree Algorithm II

Ch. 6

10/2

Review

 

 

10/4

Statistical Concepts I

Ch. 8

Sample-midterm

10/7-9

Statistical Concepts II

Ch. 8

10/11

Learning as Inference I

Ch 9.1-9.3

 

10/14

Review

 

 

 

 

 

10/16

Midterm 1

10/18, 21

    Learning as Inference II

Ch 9.4

Homework 5

(due 10/28)

10/23, 25

Structure Learning I

Ch 9.5

10/28

Structure Learning II

Ch. 9.6

Homework 6

(due 11/18)

10/30, 11/1

Learning with Hidden Variables

Ch. 11.1-2

11/4

Machine Learning

Ch. 13.1

11/6

Supervised Learning

Ch. 13

11/8

K-nearest neighbor

Ch. 14

11/11

Mixture Models

Ch. 20

Homework 7

(To be discussed on 12/2)

11/13, 15

Linear Dimension Reduction

Ch. 15, 16

11/18

Linear & Logistic Regression

SVM

Bayesian Linear Models

(By Ju Shen)

Ch. 17

11/20

Ch. 17

11/22

Ch. 18

11/25

Discussion of final project

Ch. 23

Homework 8

(optional)

12/2

Review, take-home midterm

12/4

HMM

Ch. 23

12/6

Sampling

Ch. 27

12/9

12/11

Deterministic Approximate Inference

Ch. 27

12/13

In-class Final Demonstration