In this course, we will cover probabilistic graphical models: powerful and interpretable models for reasoning under uncertainty. We will survey a family of models, such as Bayesian networks, Markov networks, and factor graphs. The discussions will include both theoretical aspects of representation, learning, and inference in graphical models, and their applications to many interesting fields such as computer vision, natural language processing, computational biology, medical diagnosis, and more. Time permitting, we will also discuss specific graphical model representations such as Hidden Markov Models, Probabilistic Relational Models, Conditional Random Fields, etc.
Time and Location: Tue  Thu 1:50  3:05pm
in Stuart 225
Professor: Mustafa
Bilgic
Office: Stuart 228C
Email: mbilgic AT
iit.edu
Office Hours: Tue 3:304:30pm (Other times by
appointment)
The
class will consist of
a mixture of presentations and class discusions. As such, students are
expected to read the required materials and participate in the
discussions. Students are expected to return a short writeup on
each reading by 11:59pm the day before the lecture. The purpose of this
class is
to learn about graphical models and
apply them to research/application problems of your
choice. You will be
expected to carryout a semester long project. The course will be fun,
a worthwile learning experience, and hopefully useful for your
research problems.
There
is a required (see
important note below) text
book for this course:
Probabilistic
Graphical Models,
by Daphne Koller and Nir Friedman
There will be additional supplemental materials (mostly available on the web).
Important Note: If the cost of the textbook is preventing you
from
registering for the course, please contact me. A copy of the book is
also expected to be available in the library reserves.
There
is a course mailing
list (pgmf10@mailer). Announcements will be made to this list. Join
using this link.
Date

Topic  Notes and Readings 
8/24

Course logistics  Syllabus Assignment 1 due 8/25, 11:59pm 
8/26

Introduction  Chapter 1 
8/31

Background Material  Chapter 2 Assignment 2 due 9/1, 6:00pm 
9/2, 9/7, 9/9

Representation  Bayesian networks  Chapter 3 Assignment 3, due 9/8, 6:00pm Hugin file for the student example (Figure 3.4) 
9/14

Representation  CPDs 
Chapter 5 
9/16, 9/21

Representation  Markov networks 
Chapter 4, Assignment 4, due 9/15, 6:00pm 
9/23

Application showdown 
Assignment 5, due 9/22, 6:00pm 
9/28, 9/30

Inference  Variable elimination 
Sections 9.1, 9.2, and 9.3. Assignment 6, due 9/29, 6pm. 
10/5, 7, 12

Inference  Message passing 
Assignment
7, due in class on 10/5. 
10/14

Inference
 MAP Inference 

10/19

Inference  Sampling 
Sections 12.1, 12.2, 12.3 Project Proposal, due in class, 10/19 
10/21

Inference, Algorithm evaluation 

10/26

Proposal comments, my research
area 
Project proposal comments due 
10/28

Learning  Overview 
Chapter 16 
11/02

Learning  Bayesian
Networks 

11/09

Learning  Markov Networks 

11/16

Templatebased representations 

11/23

Templatebased representations 

11/30

Project presentations 
