
CSL864: Special Topics in Artificial Intelligence: Probabilistic Graphical Models
Instructor: Parag Singla (email: parags AT cse.iitd.ac.in)
Class Timings/Venue:
 Time: Tue,Thu, Fri. 11:00  12:00
 Venue: Bharti 204
Office Hours:
 Time: Tuesday 12:30 pm to 1:15 pm
 Venue: Bharti 209
Teaching Assistant: Dinesh Khandelwal (email: csz138294 AT cse.iitd.ac.in)
Announcements
 [Mon Mar 31]: Assignment 2 (Part B) is out (details below)!
 [Sat Mar 15]: Minor 2 Rescheduled! New Date/Time: Wed Mar 26, 2 pm  3 pm. Venue: Bharti 501.
 [Sun Mar 9]: Assignment 2 (Part A) is now out (see below)!
 [Sun Feb 16]: Assignment 1 has been updated (see below)!
 [Sun Jan 26]: Assignment 1 is out! Details below.
 [Sun Jan 26]: Class Timing have changed! We will run the class
in the regular F slot hours now.
Tue, Thu,Fri: 11:00  12:00.
 [Sat Jan 18]: In Class Quiz: Tuesday January 28. Duration: Half an Hour (Approx).
Syllabus: Material Covered up to the Class Preceding Jan 28.
 The Class Timings and the Venue has been finalized now! Check above for details.
 [Sat Jan 4]: Next Class will be held on Tuesday Jan 7 from
2 pm to 3:30 1:30 pm to 3:00 pm.
Obective:
This course is meant to be the first graduate level course in the area of Probabilistic
Graphical Models (PGM). PGMs have emerged as a very important research field during
last decade or so with wide range of applications including Computer Vision, Information
Retrieval, Natural Language Processing, Biology and Robotics. This course aims to provide
students with a comprehensive overview of PGMs. The course content will include introduction
to Probabilistic Graphical Models, directed and undirected representations, inference and
learning algorithms and practical applications. This course is also meant to provide the
required background for pursuing research in this area.
Content: Basics: Introduction. Undirected and Directed Graphical Models. Bayesian
Networks. Markov Networks. Exponential Family Models. Factor Graph Representation. Hidden
Markov Models. Conditional Random Fields. Triangulation and Chordal Graphs. Other
pecial Cases: Chains, Trees. Inference: Variable Elimination (Sum Product and MaxProduct).
Junction Tree Algorithm. Forward Backward Algorithm (for HMMs). Loopy Belief Propagation.
Markov Chain Monte Carlo. Metropolis Hastings. Importance Sampling. Gibbs Sampling. Variational
Inference. Learning: Discriminative Vs. Generative Learning. Parameter Estimation in Bayesian
and Markov Networks. Structure Learning. EM: Handling Missing Data. Applications in Vision,
Web/IR, NLP and Biology. Advanced Topics: Statistical Relational Learning, Markov Logic Networks.
Note: All the topics above may not be covered in the course.
WeekWise Schedule
Week  Topic  Book Chapters  Class Notes/ Supplementary Material
 1  Introduction, Basics  KF Chapter 1, 2 
Introduction

2  Bayesian Networks  KF Chapter 3 
Bayes Net1
Bayes Net2

3  Markov Networks  KF Chapter 4 
Markov Network1
Markov Network2

4,5  Factor Graph Representation, HMMs, CRFs, Expoential Family 
KF Chapter 4,8 
Factor Graphs Log Linear Models
Hidden Markov Models Condiation Random Fields

6,7  Exact Inference: Variable Elimination
 KF Chapter 9 
Variable Elimination1
Variable Elimination2

8,9,10  Exact Inference: Junction Tree Algorithm. Belief Propagation (Loopy or not)
 KF Chapter 10,11 
Clique Tree Message Passing1
Clique Tree Message Passing2
Cique Tree Message Passing3
Loopy Belief Propagation
MaxProduct Belief Propagation

11,12  Sampling Based Approximate Inference: MCMC, Metropolis Hastings,
Gibbs Sampling, Importance Sampling  KF Chapter 12 
Sampling Based Inference  Basics
Forward Sampling, Likelihood Weighting, Importance Sampling
Importance Sampling
Markov Chain Monte Carlo
Markov Chain Monte Carlo1
Gibbs Sampling, Metropolis Hastings
Gibbs Sampling  Additional Notes

13  Learning: Overview, Learning in Bayesian Networks,
Learning in Markov Networks
 KF Chapter 16,17,19 
Parameter Estimation, Expectation Maximization

14  Advanced Topics/Applications/Revision   
Template: Latex Source File,
Sample Pdf File
Topic  Notes 
Probability  prob.pdf
Borrowed from Andrew Ng's Machine Learning Course at Stanford 
References
 Probabilistic Graphical Models: Principles and Techniques. Daphne Koller and Nir
Friedman. First Edition, MIT Press, 2009.
 Learning in Graphical Models. Michael Jordan (ed.). MIT Press, 1998. Collection
of Papers.
 Probabilistic Reasoning in Intelligent Systems. Judea Pearl. Morgan Kaufmann, 1988.
Other Places where a Similar Course is Offered
Assignment Submission Instructions
 You are free to discuss the problems with other students in the class. You should include the
names of the people you had a significant discussion with in your submission.
 All your solutions should be produced independently without referring to any
discussion notes or the code someone else would have written.
 All the nonprogramming solutions should be submitted using a hard copy. If you are writing
by hand, write legibly.
 Required code should be submitted using Sakai Page.
 You should archive all your submission (code) in one single zip file. This zip file
should be named as "yourentrynumber_firstname_lastname.zip". For example, if your entry number is
"2008anz7535" and your name is "Nilesh Pathak", your submission should be named as
"2008anz7535_nilesh_pathak.zip
 Honor Code: Any cases of copying will be awarded a zero on the assignment. An
additional penalty of 5 points will also be imposed (on the total course points
out of 100). More severe penalties may follow.
 Late Policy: You will lose 20% for each late day in submission. Maximum of 2 days late submissions are allowed.
Assignments
Assignment 2
 Part B. Due Date: Sunday April 27, 11:50 pm. (On Sakai).
 Part A. Due Date: Thursday April 17 (In Class).
Assignment 1. Updated Feb 21.
Due Date:
 Theoretical Questions: Tue Feb 25 (in Class).
 Implementation Questions: Wed Feb 26. 11:50 pm.
Grading Policy
Assignments (2)  12% each 
Quizzes (2)  3% each 
Minors (2)  15% each 
Major  40% 
