Brown CS 242

Previous Courses at Brown University

• CS 242: Probabilistic Graphical Models, Fall 2014.
• CS 295P: Probabilistic Graphical Models, Spring 2013.
• CS 295P: Applied Bayesian Nonparametrics, Fall 2011.
• CS 295P: Learning & Inference in Probabilistic Graphical Models, Spring 2010.

Courses at Other Universities

• Statistical Learning Theory: Graphical Models. UC Berkeley EECS 281a, Martin Wainwright, Fall 2012.
• Probabilistic Graphical Models. Stanford University, Daphne Koller, Coursera.
• Algorithms for Inference. MIT 6.438, William Freeman and Gregory Wornell, Fall 2009.
• Foundations of Probabilistic Modeling. Princeton COS513, David Blei, Fall 2010.
• Probabilistic and Unsupervised Learning. Gatsby Unit, Yee Whye Teh and Maneesh Sahani, 2009.
• Statistical Computing: Monte Carlo Methods. University of British Columbia CPSC 535d, Arnaud Doucet, 2007.

Online Tutorials & Textbooks

• A Brief Introduction to Graphical Models and Bayesian Networks. Kevin Murphy, University of British Columbia, 1998.
• A Short Course on Graphical Models. Mark Paskin, Intel Berkeley Research Center, Summer 2003.
• Graphical models and variational methods: Message-passing and relaxations. Martin Wainwright, ICML 2008.
• Probabilistic Models for Unsupervised Learning. Zoubin Ghahramani and Sam Roweis, NIPS 1999.
• Information Theory, Inference, and Learning Algorithms. D. MacKay, Cambridge University Press, 2003.
• The Elements of Statistical Learning. T. Hastie, R. Tibshirani, & J. Friedman, Springer, 2009.
• Convex Optimization. S. Boyd & L. Vandenberghe, Cambridge University Press, 2004.

Reference Print Textbooks

• Probabilistic Graphical Models: Principles and Techniques. D. Koller & N. Friedman, MIT Press, 2009.
• Machine Learning: A Probabilistic Perspective. K. Murphy, MIT Press, 2012.
• Pattern Recognition and Machine Learning. C. Bishop, Springer, 2007.
• Probabilistic Networks and Expert Systems. R. Cowell, P. Dawid, S. Lauritzen, & D. Spiegelhalter, Springer, 2002.
• Probabilistic Reasoning in Intelligent Systems. J. Pearl, Elsevier, 1988.
• Causality. J. Pearl, Cambridge University Press, 2009.
• Graphical Models. S. Lauritzen, Oxford Statistical Science Series, 1996.
• Bayesian Data Analysis. A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari, & D. Rubin, CRC Press, 2013.
• Monte Carlo Statistical Methods. C. Robert & G. Casella, Springer, 2004.
• Sequential Monte Carlo Methods in Practice. A. Doucet, N. de Freitas, & N. Gordon, editors, Springer, 2001.

Software

• Matlab Graphical Model Toolboxes. Kevin Murphy, University of British Columbia.
• UGM: Undirected Graphical Models in Matlab. Mark Schmidt, UBC.
• OpenGM2. Heidelberg Collaboratory for Image Processing.
• Dimple. Lyric Labs, Analog Devices.
• Stan. Open source system for probabilistic programming.
• Middlebury MRF Minimization Benchmark. Middlebury University.
• FastInf. Hebrew University of Jerusalem.
• Infer.NET. Tom Minka and John Winn, Microsoft Research.
• Bayesian Modeling and Monte Carlo Methods. Radford Neal, University of Toronto.
• PNL: Probabilistic Networks Library.
• BUGS: Bayesian Inference using Gibbs Sampling.
• VIBES: Variational Inference for Bayesian Networks. John Winn, Microsoft Research.
• libDAI: Discrete Approximate Inference in Graphical Models. Joris Mooij, MPI.