Course Calendar
The acronyms which identify the primary course readings are defined below.
| Date | Topic | Primary Readings | Supplemental Readings | Materials |
|---|---|---|---|---|
| 1/24 | Course Overview | Jordan 2004 | Murphy Tutorial | slides |
| 1/29 | Directed Graphical Models | MJ: 2.1, 2.3 ES: 2.2 |
Charniak 1991 | slides |
| 1/31 | Undirected Graphical Models | MJ: 2.2 ES: 2.2 |
W&J: 2.1-2.4 | slides |
| 2/05 | Inference in Graphical Models, Elimination Algorithms |
MJ: 3 ES: 2.2.5 |
W&J: 2.5 | slides |
| 2/07 | Message Passing, Belief Propagation, & Factor Graphs |
MJ: 4.1-4.2 ES: 2.3.2, pp. 69-75 |
Kschischang et al. 2001 Aji, McEliece 2000 |
slides |
| 2/12 | Bayes & Frequentist Learning from Complete Observations |
MJ: 5.1-5.2 MJ: 9.1-9.2 |
Heckerman 1999: 1-5 | slides |
| 2/14 | Exponential Families, Conjugate Priors |
MJ: 8.1, 19 ES: 2.1 |
W&J: 3.1-3.5 | slides |
| 2/19 | Holiday: No Lecture | |||
| 2/21 | Expectation Maximization (EM) in Directed Graphical Models |
MJ: 11, 12 ES: 2.3.3 |
Neal, Hinton 1999 Heckerman 1999: 6 |
slides |
| 2/26 | EM Algorithm Continued, Learning in Undirected Graphs |
MJ: 11, 12 MJ: 9.3 |
W&J: 6.1-6.2 | slides |
| 2/28 | Junction Tree Algorithm | MJ: 17 | Paskin 2003 Cowell 1999, pp. 9-37 |
slides |
| 3/05 | Undirected Gaussian Markov Random Fields |
Sudderth 2002, Chap. 2 | MJ: 13 Szeliski 1990 |
slides |
| 3/07 | State Space Models, Kalman Filter, Gaussian BP |
MJ: 15 Weiss, Freeman 2001: 1-2 |
MJ: 14 Roweis, Ghahramani 1999 |
slides |
| 3/12 | Learning in Gaussian Models, Non-Gaussian Inference |
MJ: 14 ES: 3.1 |
Ghahramani, Hinton 1996 | slides |
| 3/14 | Monte Carlo, Rejection & Importance Sampling |
Andrieu et al. 2003: 1-2 ES: 2.4 |
MacKay 1999: 1-3 | slides |
| 3/19 | Particle Filters, Sequential Monte Carlo |
Cappe et al. 2007 Andrieu et al. 2003: 4.3 |
Hamze, de Freitas 2006 Del Moral et al. 2006 |
slides |
| 3/21 | Markov Chain Monte Carlo, Metropolis & Gibbs Samplers |
Andrieu et al. 2003: 3 | MacKay 1999: 4-7 Geman, Geman 1984 |
slides |
| 3/26 | Spring Break: No Lecture | |||
| 3/28 | Spring Break: No Lecture | |||
| 4/02 | MCMC Mixing & Diagnostics, Collapsed Gibbs Samplers |
ES: 2.4.4 Andrieu et al. 2003: 3 |
Griffiths, Steyvers 2004 | slides |
| 4/04 | Mean Field Methods, Variational Learning |
Jordan et al. 1999: 4, 6 W&J: 5, 6.3 |
Winn, Bishop 2005 Blei, Ng, Jordan 2003 |
slides |
| 4/09 | Variational Learning, Blocked Gibbs Samplers |
ES: 2.3 | Scott 2002 | slides |
| 4/11 | Structured Variational Methods, Bethe Approximations, Loopy BP |
Yedidia et al. 2002 | W&J: 4.1 | slides |
| 4/16 | Convexity and Duality in Variational Methods |
W&J: 3.4-3.6, 5.3-5.4 | ES: 2.3 | slides |
| 4/18 | Reparameterization & Loopy BP, Reweighted Sum-Product |
W&J: 4.1, 7.1-7.2 | Ihler et al. 2005 Mooij, Kappen 2007 |
slides |
| 4/23 | Reweighted BP, Convex Learning Surrogates |
W&J: 7.4-7.5 | Wainwright 2006 | slides |
| 4/25 | Conditional Random Fields, MAP Estimation & Max-Product |
Sutton, McCallum 2012 MJ: 4.3 |
Aji, McEliece 2000 | slides |
| 4/30 | Reweighted Max-Product, Course Review & Outlook |
W&J: 8.1-8.4 | Yanover et al. 2006 Greig et al. 1989 |
slides |
| 5/02 | Project Help Session | |||
| 5/07 | Project Presentations | |||
| 5/13 | Project Reports Due |
Readings
Primary Resources
MJ: M. Jordan, An Introduction to Probabilistic Graphical Models. Printed reader available at the Metcalf Copy Center.
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.
Surveys & Tutorials
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.
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.
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.
D. Heckerman, A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models, M. Jordan, MIT Press, 1999.
M. Jordan, Z. Ghahramani, T. Jaakkola, & L. Saul, An Introduction to Variational Methods for Graphical Models. Machine Learning, vol. 37, pp. 183-233, 1999.
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.
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.
S. Roweis & Z. Ghahramani, A Unifying Review of Linear Gaussian Models. Neural Computation, vol. 11, pp. 305-345, 1999.
E. Sudderth, Embedded Trees: Estimation of Gaussian Processes on Graphs with Cycles, Chapter 2: Background. Masters Thesis, Massachusetts Institute of Technology, Feb. 2002.
C. Sutton & A. McCallum, An Introduction to Conditional Random Fields. Foundations and Trends in Machine Learning, vol. 4, pp. 267-373, 2012.
Y. Teh & M. Jordan, Hierarchical Bayesian Nonparametric Models with Applications. To appear in Bayesian Nonparametrics in Practice, Cambridge University Press, 2010.
A. Willsky, Multiresolution Markov Models for Signal and Image Processing. Proceedings of the IEEE, vol. 90, pp. 1396-1458, 2002.
J. Yedidia, W. Freeman, & Y. Weiss, Understanding Belief Propagation and its Generalizations. Exploring Artificial Intelligence in the New Millennium, Morgan Kaufmann, 2002.
Research Articles
D. Blei, A. Ng, & M. Jordan, Latent Dirichlet Allocation. Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003.
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.
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.
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.
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.
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.
T. Jaakkola & M. Jordan, Bayesian Parameter Estimation via Variational Methods. Statistics and Computing, vol. 10, pp. 25-37, 2000.
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.
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.
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.
R. Szeliski, Bayesian Modeling of Uncertainty in Low-Level Vision. International Journal of Computer Vision, vol. 5, pp. 271-301, 1990.
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.
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, Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms. IEEE Transactions on Information Theory, vol. 51, pp. 2282-2312, 2005.