Probabilistic Graphical Models
Spring 2011
Overview A graphical model is a probabilistic model, where the conditional dependencies between the random variables is specified via a graph. Graphical models provide a flexible framework for modeling large collection of variables with complex interactions, as evidenced by their wide domain of application, including for example machine learning, computer vision, speech and computational biology. This course will provide a comprehensive survey of learning and inference methods in graphical models, including variational methods, primaldual methods and sampling techniques. 
General information Lecture: MWF 1:302:20pm Grading: exercises (50 %) + exam (50 %) Book: Probabilistic graphical models: principles and techniques. Daphne Koller and Nir Friedman  MIT Press (2009) EXAM: Friday June 10 at 10am. Will last for approximately 2.5h. 
Syllabus

Schedule
Lecture  Date  Topic  Slides 
Instructor  Readings  Assignments 
1  March 28  Introduction  lecture1.pdf  Tamir  Chapter 2 

2  March 30  Bayesian Networks I  lecture2.pdf  Tamir  Chapter 3 

3  April 1  Bayesian Networks II  lecture3.pdf  Tamir  Chapter 3 

4  April 4  Undirected Graphical Models I

lecture4.pdf  Raquel  Chapter 4 

5  April 6  NO CLASS

     

6  April 8  Undirected Graphical Models II

lecture5.pdf  Raquel  Chapter 4 

7  April 11  Chordal Graphs, CRFs

lecture6.pdf  Raquel  Chapter 4 

8  April 13  Exponential Family

lecture7.pdf  Tamir  Chapter 8  ex1 corrected typo! due April 20 at 1:30pm 
9  April 15  NO CLASS: Snowbird

     

10  April 18  Exact inference I: VE

lecture8.pdf  Raquel  Chapter 9 

11  April 20  Exact inference II: VE

lecture9.pdf  Raquel  Chapter 9 

12  April 22  Exact inference III: Conditioning

lecture10.pdf  Raquel  Chapter 9 

13  April 25  Exact inference IV: Clique Trees

lecture11.pdf  Raquel  Chapter 10 

14  April 27  Exact inference V: Message passing I

lecture12.pdf  Raquel  Chapter 10 

15  April 29  Exact inference VI: Message passing II

lecture13.pdf  Raquel  Chapter 10 

16  May 2  Inference via optimization I

lecture14.pdf  Raquel  Chapter 11 

17  May 4  Inference via optimization II

lecture15.pdf  Tamir  Chapter 11 

18  May 6  Inference via optimization III

lecture16.pdf  Tamir  Chapter 11  ex2 due May 13 at 1:30pm 
19  May 9  Inference via sampling I

lecture17.pdf  Tamir  Chapter 12 

20  May 11  Inference via sampling II

lecture18.pdf  Tamir  Chapter 12 

21  May 13  Inference via sampling III

lecture19.pdf  Tamir  Chapter 12 

22  May 16  MAP estimation I

lecture20.pdf  Tamir  Chapter 13 

23  May 18  MAP estimation II

lecture21.pdf  Tamir  Chapter 13 

24  May 20  MAP estimation III

lecture22.pdf  Tamir  Chapter 13 

25  May 23  Introduction to learning

lecture23.pdf  Raquel  Chapter 16  ex3 due May 30 at 1:30pm 
26  May 25  Learning I

lecture24.pdf  Tamir  Notes 

27  May 25  Learning II

lecture27.pdf  Tamir  Notes 

28  May 27  Learning III

lecture27.pdf  Tamir  Notes 

29  May 30  NO CLASS



30  June 1  Learning IV

lecture28.pdf  Tamir  Notes  ex4 due June 7 at 1:30pm 
31  June 3  Learning V

lecture29.pdf  Tamir  Notes 
