Markoviana: A reading group
On Probabilistic Reasoning in AI
Spring 2005
Friday 2:30-4:30pm. BY 576
Coordinated by:� Subbarao Kambhampati
Weekly meetings. Each meeting approx ~2 hours.
Students encouraged to register for at least 1 credit of independent study (mostly as a� sign of serious commitment).
(you can take it also for 2 credits�which will involve making one presentation at least
and 3 credits which will involve making 2 presentations. The grade will be Pass/Fail.)
Everyone is expected to read the week�s material before showing up.
This is not a formal
lecture course. We will read papers/chapters together and participants
�will give
presentations on various weeks.
Each participant must lead at least one reading session.�
Current schedule:
���� 1/28:� R&N Chapter 15, Sections 15.1-15.3. Speaker: Will Cushing. Here are the notes.
����� 2/4:�� Rabiner�s tutorial on HMMs (also the chapter on HMMs from Durbin et al). Speaker: Fatih Gelgi.� Here are the notes
���� 2/11:� Kalman Filters (R&N Chapter 15, Sections 15.4 + Kalman filter tutorial). Speaker: Srini Vadrevu. Here are the notes.
���� 2/18:� Particle filters (R&N 15.5 and Particle Filter Tutorial). Speaker: Will Cushing. Here are Slides used
����� 2/25:
����� 3/4: Statistical Learning � R&N 20 (1,2)� (Heckerman Tutorial is another reference) [ Jicheng's Slides] (A more complete but uncommented set is here)
� ���3/11 :� Statistical learning (EM algorithm) R&N 20.3 + Dellaert's paper). [R&N slides on EM]. [Fatih's Frank Dellart Slides]
����� 3/18:� Spring break!
������3/25:� Cancelled due to AAAI exhaustion
����� 4/1:� Structural Probabilistic Relational Models: Inference/Learning (R&N 14.6 + Getoor et. al. chapter) [NamTran]
���� 4/8:� Markov Networks (undirected graphical models) (Koller & Friedman, Chapter 5)[Srini Vadrevu]
���� 4/15: Conditional Random Fields
���
���� 4/22:� POMDP� (paper?) [Dan Bryce]
���� 4/29:�� Markov Games
========================
� Kernel methods�Support vector machines� (paper TBDr) [Mariano]
� Utility and MDPs (R&N 16 and 17) [Menkes]
�� Reinforcement learning� (R&N 21) [J Benton]
����� (semester ends 5/3)
�����
Related Sites:
- Pedro Domingo�s course in U. Wash seems very relevant..
- Max Welling�s Machine learning notes are also quite useful
- See also his Caltech course notes� and his Kernel notes..
- Tom Dietterich�s course on probabilistic relational models
- Brian William�s course on robotics/planning
- SLAM summer School
------------Expected Evolution
|
Topic |
|
Speakers |
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|
Filtering and estimation � --Hidden Markov Models � --Kalman Filters � --Particle Filters � --Plan recognition |
R&N chapter on Temporal
reasoning Chapter 15 (15.1-15.5) [2 weeks] ] Paper on particle filters and kalman filters AAAI 2004 best paper VLDB 2004 best paper |
Srini Vadrevu |
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|
Statistical learning � EM etc Kernel machines and support
vector machines |
R&N chapter 20 Source for Kernel m/c? |
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MDP and POMDP �� --MDP methods �� --POMDP basic solution methods |
MDP: �
LRTDP or ICAPS 2005 paper from CMU POMDP?? |
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Reinforcement learning� |
R&N chapter on reinforcement
learning |
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Markov Games � (Multi-agent MDPs) |
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Bayes Nets; MCMC etc First-order probabilistic
inference Markov Random Fields |
From R&N and Koller book |
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