Markoviana: A reading group
On Probabilistic Reasoning in AI
Spring 2005
Friday 2:304:30pm. 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 15.115.3. 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:
Expected Evolution
Topic 

Speakers 

Filtering and estimation Hidden Markov Models Kalman Filters Particle Filters Plan recognition 
R&N chapter on Temporal
reasoning Chapter 15 (15.115.5) [2 weeks] ] Paper on particle filters and kalman filters AAAI 2004 best paper VLDB 2004 best paper 
Srini Vadrevu 

Statistical learning EM etc Kernel machines and support
vector machines 
R&N chapter 20 Source for Kernel m/c? 


MDP and POMDP MDP methods POMDP basic solution methods 
MDP:
LRTDP or ICAPS 2005 paper from CMU POMDP?? 


Reinforcement learning 
R&N chapter on reinforcement
learning 


Markov Games (Multiagent MDPs) 



Bayes Nets; MCMC etc Firstorder probabilistic
inference Markov Random Fields 
From R&N and Koller book 





