**Seminar on Probabilistic Graphical Models, fall 15/16**

**Haim****
Kaplan **

**We will follow some chapters of the book on Probabilistic
Graphical Models by Koller and Friedman (MIT
press 2009). The book has three parts and we will cover a few chapters from
each part. You may want to read the probabilistic background in chapter 2 which
we will skip.**

**Tentative
schedule **

**1. An introductory lecture
(Oct 20)**

**2. Chapter 3: The Bayesian Network Representation (Oct 27),
****Omer Tabach****, Omer's notes, Homework #1**

**3. Chapter 4: Undirected Graphical Models (Nov 3), ****Itzhak**** Taub****, Presentation,
Homework
#2**

**4. Chapter 9: Exact Inference: Variable Elimination (Nov
10), ****Barak Sternberg**** and ****Shimi**** Salant****, Presentation parts 1+3, Presentation part 2, Homework #3**

**5. Chapter 10: Exact Inference: Clique Trees (Nov 17), ****Jonathan
Shafer****, Lecture notes, Handout, Homework #4**

**6. Chapter 11: Inference as Optimization (Nov 24), ****Daniel
Carmon****, slides,
homework #5**

**7. Chapter 12: Particle-Based Approximate Inference (Dec
1), ****Dafna**** Sade**** and ****Uri Meir****, presentation, homework #6**

**8. Chapter 13: MAP Inference (Dec 8), ****Alon**** Brutzkus****, presentation, homework #7**

**9. Chapter 17: Parameter Estimation (Dec 15), ****Tomer**** Galanti****, presentation, homework
#8**

**10. Chapter 18: Structure Learning in Bayesian Networks
(Dec 22), ****Guy Shalev****, presentation, homework
#9**

**11. Chapter 19: Partially Observed Data (Dec 29), ****Tal Gerbi**** and ****Shay Kazaz****, presentation1,
presentation2, homework**

**12. Chapter 20: Learning Undirected Models (Jan 5), ****Barak
Gross, Mark Berlin****, presentation1,
presentation2**

**13. Application
(speech recognition?) (Jan 12), ****Amichy**** Painsky****, presentation**