| Lecture |
Topics |
Complementary
Readings |
Assigned
Readings |
| Sept 14 |
Course overview |
||
| Sept 16 |
Bayesian networks, exact
inference (Lecture slides) |
[KF] Sect 3.1-3.2, 9.1-9.4 |
|
| Sept 21 |
Conditional independence,
approximate inference (Monte Carlo techniques (Lecture slides) |
[KF] Sect 3.3, 12.1-12.3 |
|
| Sept 23 |
Lecture canceled |
||
| Sept 28 |
Monte Carlo techniques continued |
||
| Sept 30 |
Statistical learning (Bayesian
learning, maximum likelihood, maximum a posteriori hypothesis) (Lecture slides) |
[KF] Sect 17.1-17.4 |
|
| Oct 5 |
Learning from incomplete data,
expectation maximization (Lecture
slides) |
[KF]Sect 19.1-19.2 |
|
| Oct 7 |
Single/multi parameter models,
conjugate prior |
[GCSR] Chapter 2-3 | |
| Oct 12 |
1) Friedman, Linial, Nachman and
Pe'er, Using
Bayesian
networks
to
analyze
expression
data, Journal of
computational biology, 2000 Presenters: Catalin-Alexandru Avram, Muhammad Bilal Sheikh 2) Jaakola and Jordan, Variational probabilistic inference and the QMR-DT network, JAIR, 1999 Presenters: Anup Kumar Chalamalla, Jalaj Kumar Upadhyay |
||
| Oct 14 |
Single/multi parameter models, conjugate prior, exchangeability, | [GCSR] Chapter 2-3 | |
| Oct 19 |
3) Blei, Ng and Jordan, Latent
Dirichlet
Allocation, JMLR, 2003 Presenters: Wenxuan Wang, Gelin Zhou 4) Fei-Fei and Perona, A Bayesian hierarchical model for learning natural scene categories, CVPR, 2005 Presenters: Clara Ines Forero Suancha, Wenye Yu |
||
| Oct 21 |
Informative/non-informative priors, mixture models, Bayesian clustering, Dirichlet process (aka the Chinese restaurant process) | [GCSR] Chapter 5 | |
| Oct 26 |
5) Rasmussen, Infinite
Gaussian
Mixture
Model, NIPS, 2000 Presenters: Jiong Xi, 6) Navarro, Griffiths, Steyvers and Lee, Modeling individual differences using Dirichlet processes, Journal of Mathematical Psychology, 2006 Presenters: Anthony Schmieder, Tommy Carpenter, Steffen Kellen |
||
| Oct 28 |
Hierarchical Dirichlet process
(aka the Chinese restaurant franchise) Pitman-Yor process |
Project proposal due | |
| Nov 2 |
7) Teh, Jordan, Beal and Blei, Hierarchical
Dirichlet
processes, Journal of the American Statistical
Association, 2006 Presenters: Arthur Carvalho, Hu Bo 8) Wood, Archambeau, Gasthaus, James and Teh, A stochastic Memoizer for Sequence Data, ICML, 2009 Presenters: Karim Hamdan Ali, Sarah Nadi |
||
| Nov 4 |
Beta process (aka the Indian
buffet process) |
||
| Nov 9 |
Lecture canceled |
||
| Nov 11 |
9) Ghahramani, Griffiths and
Sollich, Bayesian
nonparametric
latent
feature
models, Bayesian Statistics, 2007 Presenters: Tao Wang 10) Van Gael, Teh and Ghahramani, The Infinite Factorial Hidden Markov Model, NIPS, 2009 |
||
| Nov 16 |
Regression with Gaussian
processes |
||
| Nov 18 |
11) Lizotte, Wang, Bowling and
Schuurmans, Automatic
Gait
Optimization
with
Gaussian
Process
Regression,
IJCAI, 2007 Presenters: Md Faizul Bari, Md Haque 12) Ko and Fox, GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models, Autonomous Robots Journal, 2009 Presenters: Vladimir Pisanov |
||
| Nov 23 |
Bayesian Reinforcement Learning (Lecture Slides) |
||
| Nov 25 |
13) Engel, Mannor, Meir, Bayes
meets Bellman: The Gaussian process approach to temporal difference
learning, ICML, 2003 Presenters: Alexander Leong, Simon Benjamin Orion Parent 14) Poupart, Vlassis, Hoey and Regan, An Analytic Solution to Discrete Bayesian Reinforcement Learning, ICML, 2006 Presenters: Shehroz Khan, Dmitry Pyryeskin, Fady Samuel |
||
| Nov 30 |
Gaussian Process dimensionality
reduction |
||
| Dec 2 |
15) Lawrence, Probabilistic
Non-linear
Principal
Component
Analysis
with
Gaussian Process Latent
Variable Models, JMLR, 2005 Presenters: Ryan James Case, Chen Li, Aditya Tayal 16) Wang, Fleet, Hertzmann, Gaussian Process Dynamical Models for Human Motion, IEEE Transactions on Parttern Analysis and Machine Intelligence, 2008 |