Course Calendar
The acronyms which identify the primary course readings are defined below.
For readings marked with a bold (C), critical comments are due before the class meeting.
| Date | Topic | Assigned Readings | Supplemental Readings | Lecture Materials |
|---|---|---|---|---|
| 1/27 | Course Overview | Jordan 2004 | K&F: 1, 2.1, 2.2 | Sudderth: slides |
| 2/01 | Directed Graphical Models | K&F: 3.1 to 3.3 | Charniak 1991 K&F: 5.4, 6.2, 6.4.1 |
Sudderth |
| 2/03 | Undirected Graphical Models | K&F: 4.1 to 4.3 W&J: 2.1 to 2.4 |
K&F: 4.4 ES: 2.2 |
Sudderth: slides |
| 2/08 | Variable Elimination, Message Passing |
ES: 2.3.2, pp. 69-75 K&F: 9.2, 9.3, 10.1, 10.2.1 |
K&F: 9.1, 9.4 W&J: 2.5 |
Sudderth: slides |
| 2/10 | Message Passing, Junction Trees |
Kschischang et al. 2001 (C) Cowell 1999, pp. 9-37 (C) |
K&F: 10.2 to 10.4 Paskin 2003 Aji, McEliece 2000 |
Pacheco: notes Black: slides |
| 2/15 | Directed Gaussian Graphical Models, Kalman Filter |
K&F: 7.1, 7.2, 15.4.1 Roweis, Ghahramani 1999 (C) |
Willsky 2002 | Sudderth Kim: slides |
| 2/17 | Kalman Filter, Gaussian Markov Random Fields |
Sudderth 2002(a), Sec. 2.1-2.2 Szeliski 1990 (C) |
K&F: 7.3 Cowell 1999, pp. 38-42 |
Sudderth Sun: slides |
| 2/22 | No Class | |||
| 2/24 | Exponential Families, Maximum Likelihood, EM Algorithm |
W&J: 3, 6.1, 6.2 Neal, Hinton 1999 (C) Heckerman 1999, Sec. 3 to 6 |
ES: 2.1.1, 2.3.3 K&F: 8.2, 8.3, 17.1 K&F: 17.2, 19.1, 19.2 |
Sudderth Zuffi: slides |
| 3/01 | Variational Methods, Mean Field |
W&J: 3.6, 5.1 to 5.4 Winn, Bishop 2005 (C) |
K&F: 11.5 ES: 2.3.1 |
Sudderth Miller: slides |
| 3/03 | Bayesian Estimation, Variational Bayes |
ES: 2.1 W&J: 6.3 Blei, Ng, Jordan 2003 (C) |
K&F: 17.3, 17.4, 19.3 Jaakkola, Jordan 2000 |
Sudderth Santhanam: slides |
| 3/08 | No Class | |||
| 3/10 | No Class | |||
| 3/15 | Loopy Belief Propagation |
W&J: 4.1 to 4.1.5 Yedidia et al. 2002 |
K&F: 11.1 to 11.3 ES: 2.3.2 |
Sudderth: slides |
| 3/17 | Generalized BP, Convergence of Loopy BP |
Yedidia et al. 2005 (C) Ihler et al. 2005(a) (C) |
W&J: 4.2 Mooij, Kappen 2007 |
Mayer: slides Wei: slides |
| 3/22 | Gaussian Belief Propagation |
Weiss, Freeman 2001 Malioutov et al. 2006 (C) |
Sudderth 2002(a), Sec. 2.3 | Sudderth: slides Ulusoy: slides |
| 3/24 | Undirected Parameter Estimation, Reweighted BP |
K&F: 20.1 to 20.4 W&J: 7.1 to 7.2.1 Wainwright 2006 (C) |
W&J: 7.2 to 7.5 K&F: 20.5 |
Sudderth Klein: slides |
| 3/29 | Spring Break | |||
| 3/31 | Spring Break | |||
| 4/05 | MAP Estimation: Max-Product, Graph Cuts |
W&J: 8.1 to 8.3 Boykov et al. 2001 |
K&F: 13.1 to 13.3, 13.6 Greig et al. 1989 |
Sudderth Tsoli: slides |
| 4/07 | Reweighted Max-Product, Linear Programming |
W&J: 8.4 Yanover et al. 2006 (C) |
K&F: 13.4 to 13.5 Weiss et al. 2007 |
Sudderth Wang: slides |
| 4/12 | Sequential Importance Sampling, Particle Filters |
K&F: 12.1-12.2 Cappe et al. 2007 (C) |
ES: 2.4-2.4.2, 3.1 K&F: 15.3 |
Sudderth Jones: slides |
| 4/14 | Sequential Monte Carlo |
Doucet et al. 2000 Hamze, de Freitas 2006 (C) |
Del Moral et al. 2006 NIPS09: de Freitas, Doucet |
Sudderth: slides Feldman |
| 4/19 | Markov Chain Monte Carlo |
Andrieu et al. 2003 K&F: 12.3 |
MacKay 1999 ES: 2.4.3-2.4.4 |
Sudderth: slides |
| 4/21 | Blocked and Collapsed Gibbs Samplers |
Scott 2002 (C) Griffiths, Steyvers 2004 (C) Griffiths et al. 2005 |
Geman, Geman 1984 | Ural: slides Mason: slides |
| 4/26 | Nonparametric Belief Propagation |
ES: 3.2 to 3.5 | ES: 4 Ihler et al. 2005(b) |
Sudderth: slides |
| 4/28 | Expectation Propagation |
W&J: 4.3 Murphy, Weiss 2001 Heskes, Zoeter 2002 (C) |
K&F: 11.4 Minka 2001 Sudderth 2002(b) |
Sudderth: slides Buller: slides |
| 5/03 | Bayesian Nonparametrics |
ES: 2.5 Neal 2000 |
Jordan 2005 Ghahramani 2005 Teh 2009 |
Sudderth |
| 5/05 | Dirichlet Process Mixture Models |
Blei, Jordan 2006 (C) Teh et al. 2006 (C) |
Teh, Jordan 2010 | Ghosh: slides Swanson: slides |
| 5/10 | Final Project Presentations |
Readings
Primary Resources
K&F: D. Koller & N. Friedman, Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
W&J: M. Wainwright & M. Jordan, Graphical Models, Exponential Families, and Variational Inference. Foundations and Trends in Machine Learning, vol. 1, pp. 1-305, 2008.
ES: E. Sudderth, Graphical Models for Visual Object Recognition and Tracking, Chapter 2: Nonparametric and Graphical Models. Doctoral Thesis, Massachusetts Institute of Technology, May 2006.
Survey, Tutorial, & Research Articles
S. Aji & R. McEliece, The Generalized Distributive Law. IEEE Transactions on Information Theory, vol. 46, pp. 325-343, 2000.
C. Andrieu, N. de Freitas, A. Doucet, & M. Jordan, An Introduction to MCMC for Machine Learning. Machine Learning, vol. 50, pp. 5-43, 2003.
D. Blei, A. Ng, & M. Jordan, Latent Dirichlet Allocation. Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
D. Blei & M. Jordan, Variational Inference for Dirichlet Process Mixtures. Bayesian Analysis, vol. 1, pp. 121-144, 2006.
Y. Boykov, O. Veksler, & R. Zabih, Fast Approximate Energy Minimization via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 1-18, 2001.
O. Cappe, S. Godsill, & E. Moulines, An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo. Proceedings of the IEEE, vol. 95, pp. 899-924, 2007.
E. Charniak, Bayesian Networks without Tears. AI Magazine, vol. 12, pp. 50-63, 1991.
R. Cowell, Introduction to Inference for Bayesian Networks, Advanced Inference in Bayesian Networks. Learning in Graphical Models, M. Jordan, MIT Press, 1999.
P. Del Moral, A. Doucet, & A. Jasra, Sequential Monte Carlo Samplers. Journal of the Royal Statistical Society B, vol. 68, pp. 411-436, 2006.
A. Doucet, N. de Freitas, K. Murphy, & S. Russell, Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. Uncertainty in Artificial Intelligence 16, pp. 176-183, 2000.
B. Frey & N. Jojic, A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1392-1416, 2005.
S. Geman & D. Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, 1984.
D. Greig, B. Porteous, & A. Seheult, Exact Maximum A Posteriori Estimation for Binary Images. Journal of the Royal Statistical Society B, vol. 51, pp. 271-279, 1989.
T. Griffiths & M. Steyvers, Finding Scientific Topics. Proceedings of the National Academy of Sciences, vol. 101, pp. 5228-5235, 2004.
T. Griffiths, M. Steyvers, D. Blei, & J. Tenenbaum, Integrating Topics and Syntax. Neural Information Processing Systems 17, pp. 537-544, 2005.
F. Hamze & N. de Freitas, Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs of Arbitrary Topology. Neural Information Processing Systems 18, pp. 491-498, 2006.
D. Heckerman, A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models, M. Jordan, MIT Press, 1999.
T. Heskes & O. Zoeter, Expectation Propagation for Approximate Inference in Dynamic Bayesian Networks. Uncertainty in Artificial Intelligence 18, pp. 216-223, 2002.
A. Ihler, J. Fisher, & A. Willsky, Loopy Belief Propagation: Convergence and Effects of Message Errors. Journal of Machine Learning Research, vol. 6, pp. 905-936, 2005 (a).
A. Ihler, J. Fisher, R. Moses, & A. Willsky, Nonparametric Belief Propagation for Self-Localization of Sensor Networks. IEEE Journal on Selected Areas in Communications, vol. 23, pp. 809-819, 2005 (b).
T. Jaakkola & M. Jordan, Bayesian Parameter Estimation via Variational Methods. Statistics and Computing, vol. 10, pp. 25-37, 2000.
M. Jordan, Graphical Models. Statistical Science, vol. 19, pp. 140-155, 2004.
F. Kschischang, B. Frey, & H.-A. Loeliger, Factor Graphs and the Sum-Product Algorithm. IEEE Transactions on Information Theory, vol. 47, pp. 498-519, 2001.
D. MacKay, Introduction to Monte Carlo Methods. Learning in Graphical Models, M. Jordan, MIT Press, 1999.
D. Malioutov, J. Johnson, & A. Willsky, Walk-Sums and Belief Propagation in Gaussian Graphical Models. Journal of Machine Learning Research, vol. 7, pp. 2031-2064, 2006.
T. Minka, Expectation Propagation for Approximate Bayesian Inference. Uncertainty in Artificial Intelligence 17, pp. 362-369, 2001.
J. Mooij & H. Kappen, Sufficient Conditions for Convergence of the Sum-Product Algorithm. IEEE Transactions on Information Theory, vol. 53, pp. 4422-4437, 2007.
K. Murphy & Y. Weiss, The Factored Frontier Algorithm for Approximate Inference in DBNs. Uncertainty in Artificial Intelligence 17, pp. 378-385, 2001.
R. Neal & G. Hinton, A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants. Learning in Graphical Models, M. Jordan, MIT Press, 1999.
R. Neal, Markov Chain Sampling Methods for Dirichlet Process Mixture Models. Journal of Computational and Graphical Statistics, vol. 9, pp. 249-265, 2000.
S. Roweis & Z. Ghahramani, A Unifying Review of Linear Gaussian Models. Neural Computation, vol. 11, pp. 305-345, 1999.
S. Scott, Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century. Journal of the American Statistical Association, vol. 97, no. 457, 2002.
E. Sudderth, Embedded Trees: Estimation of Gaussian Processes on Graphs with Cycles, Chapter 2: Background. Masters Thesis, Massachusetts Institute of Technology, Feb. 2002 (a).
E. Sudderth, Expectation Propagation. Massachusetts Institute of Technology, 2002 (b).
R. Szeliski, Bayesian Modeling of Uncertainty in Low-Level Vision. International Journal of Computer Vision, vol. 5, pp. 271-301, 1990.
Y. Teh, M. Jordan, M. Beal, & D. Blei, Hierarchical Dirichlet Processes. Journal of the American Statistical Association, vol. 101, no. 476, 2006.
Y. Teh & M. Jordan, Hierarchical Bayesian Nonparametric Models with Applications. To appear in Bayesian Nonparametrics in Practice, Cambridge University Press, 2010.
M. Wainwright, T. Jaakkola, & A. Willsky, Tree-Based Reparameterization Framework for Analysis of Sum-Product and Related Algorithms. IEEE Transactions on Information Theory, vol. 49, pp. 1120-1146, 2003.
M. Wainwright, T. Jaakkola, & A. Willsky, A New Class of Upper Bounds on the Log Partition Function. IEEE Transactions on Information Theory, vol. 51, pp. 2313-2335, 2005.
M. Wainwright, Estimating the "Wrong" Graphical Model: Benefits in the Computation-Limited Setting. Journal of Machine Learning Research, vol. 7, pp. 1829-1859, 2006.
Y. Weiss & W. Freeman, Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology. Neural Computation, vol. 13, pp. 2173-2200, 2001.
Y. Weiss, C. Yanover, & T. Meltzer, MAP Estimation, Linear Programming and Belief Propagation with Convex Free Energies. Uncertainty in Artificial Intelligence 23, pp. 416-425, 2007.
J. Winn & C. Bishop, Variational Message Passing. Journal of Machine Learning Research, vol. 6, pp. 661-694, 2005.
A. Willsky, Multiresolution Markov Models for Signal and Image Processing. Proceedings of the IEEE, vol. 90, pp. 1396-1458, 2002.
C. Yanover, T. Meltzer, & Y. Weiss, Linear Programming Relaxations and Belief Propagation - An Empirical Study. Journal of Machine Learning Research, vol. 7, pp. 1887-1907, 2006.
J. Yedidia, W. Freeman, & Y. Weiss, Understanding Belief Propagation and its Generalizations. Exploring Artificial Intelligence in the New Millennium, Morgan Kaufmann, 2002.
J. Yedidia, W. Freeman, & Y. Weiss, Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms. IEEE Transactions on Information Theory, vol. 51, pp. 2282-2312, 2005.