CS786 Spring 2012 - Schedule

This is a tentative schedule only.  As the course progresses, the schedule will be adjusted.

Reference:


Lecture
Date
Topics
Readings
Paper presentations
1
May 1
Course overview (Lecture slides) [KF] Chapters 1, 2

2
May 3
Directed graphical models: Bayesian Networks (Lecture slides) [KF] Chapter 3


3
May 8
Inference, variable elimination (Lecture slides)
[KF] Chapter 9

4
May 10
Intro to Machine Learning (Lectures slides)
[KF] Chapter 16


5
May 15
Intro to Machine Learning (no slides)

[KF] Chapter 16

6
May 17
Parameter estimation (Lecture slides)

[KF] Chapter 17
1) Exact Linkage Computation for General Pedigrees
M. Fishelson and D. Geiger
Bioinformatics, 18(1), 189-198, 2002
Presenters: Sungjoon Cho
7
May 24
Undirected graphical models: Markov networks and Conditional random fields (Lecture slides)

[KF] Chapter 4

8
May 29
Structured Potentials (no new lecture slides)
[KF] Chapter 5
2) Fuchun Peng and Andrew McCallum (2004). Accurate Information Extraction from Research Papers using Conditional Random Fields. In Proceedings of Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT/NAACL-04), 2004.
Presenters: Gaurav Baruah, Kanwaljit Singh, Filip Krynicki
9
May 31
Weighted model counting (Lecture slides)


10
Jun 5
Weighted model counting

3) Exploiting Causal Independence Using Weighted Model Counting
Wei Li, Pascal Poupart and Peter van Beek
In Proceedings of the 23rd National Conference on Artificial Intelligence (AAAI), Chicago, Illinois, 2008.
Presenters: Tzu-Yang Yu, John Morcos, Xu Chu
11
Jun 7
Inference as optimization (Lecture slides)
[KF] Chapter 11

12
Jun12
Inference as optimization (Lecture slides)
[KF] Chapter 11
4) Expectation-Propagation for the Generative Aspect Model
Thomas Minka, John Lafferty
UAI, 352-359, 2002
Presenters: Claude Richard, Elnaz Barshan Tashnizi, Sahil Singla
13
Jun 14
Sampling techniques (Lecture slides)
[KF] Chapter  12

14
Jun 19
Sampling techniques
[KF] Chapter 13
5) CONDENSATION--Conditional Density Propagation for Visual Tracking
Isard and Blake,
International Journal of Computer Vision, 1998
Presenters: Andrew Codd, Josip Pavic, Celine Craye
15
Jun 21
MAP inference (Lecture slides)
[KF] Chapter 13

16
Jun 26
MAP inference (Lecture slides)
[KF] Chapter 13
6) A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
Szeliski, R. ; Zabih, R. ;  Scharstein, D. ;  Veksler, O. ;  Kolmogorov, V. ;  Agarwala, A. ;  Tappen, M. ;  Rother, C. IEEE Transactions on Pattern Analysis and Machine Learning, 30(6), 1068-1080, 2008
Presenters: Qu Chen, Stacey Jeffery
17
Jun 28
Expectation Maximization (Lecture slides)
[KF] Chapter 19

18
Jul 3
Bayesian parameter Estimation (Lecture slides)
[KF] Chapter 17
7) Comparative Analysis of Probabilistic Models for Activity Recognition with an Instrumented Walker
Farheen Omar, Mathieu Sinn, Jakub Truszkowski, Pascal Poupart, James Tung and Allan Caine
Uncertainty in Artificial Intelligence (UAI), Catalina, CA, 2010
Presenters: Mohammad Rahman, Nazanin Mohammadi, Jinqiu Yang
19
Jul 5
Parameter Estimation for Undirected Models (Lecture slides)
[KF] Chapter 20

20
Jul 10
Neural networks (Lectures slides)
Bishop, Pattern Recognition and Machine Learning, Sections 5.2, 5.3
8) A real-time expectation-maximization algorithm for acquiring multiplanar maps of indoor environments with mobile robots
Sebastian Thurn, Christian Martin, Yufeng Liu, Dirk Hahnel, Rosemary Emery-Montemerlo, Deepayan Chakrabarti, Wolfram Burgard
IEEE Transactions on Robotics and Automation, 20(3), 433-442, 2004
Presenters: Quan Zhou, Noha Adel Elprince, Ravi Chandra
21
Jul 12
Probabilistic graphical models as neural networks (Lecture slides)


22
Jul 17
Deep Learning (Lecture slides)

9) Acoustic Modeling Using Deep Belief Networks
A. Mohamed, G.E. Dahl, G. Hinton
IEEE Transactions on Audio, Speech and Language Processing, 20(1), 14-22, 2012
Presenters: Ross Hacquebard, Andrew Cameron, David Kaufman
23
Jul 19
Markov Logic Networks (Lecture slides)


24
Jul 24
Lifted inference (Lecture slides)

10) Efficient Lifting for Online Probabilistic Inference,
Aniruddh Nath, Pedro Domingos. Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010.
Presenters: Xiang Ji, Shawn Eastwood, Amer Abdo