The project gives you the opportunity to study in greater depth some concepts of the course. The topic has to be linked with algorithms, concepts or methods presented in class, but beyond this requirement, the choice is quite open. In particular, it may be tailored to your interests. We encourage you to choose a paper that closely fits your interests, and any personal original contribution is valued.
The standard class projects need to contain the following 3 components:
An article review around a given topic (research articles or chapter from Mike's book not studied in class). See below for a list of tentative projects. This means to read and understand a specific research article.
An implementation of the method.
An experimentation with real data. This means to apply the method on real data and report your findings and observations. If the paper is quite dense and theoretical, then an experimentation on simulated / synthetic data is sufficient.
The project may be in groups of three or four. Once you have an idea of a project, it is mandatory to have it validated by the teacher by submitting a quick description of it on Studium.
The final class project counts for 30%. Evaluation will be made on:
A report (of about 4 to 8 pages) presenting the project and the obtained results (for applicative projects), to be given by December 20th, 2016 on Studium. The report has to be written in such a way that any student who has followed the class can understand (no need to introduce graphical model concepts). The report has to clearly present (in French or English) the studied problem and the existing approaches. You will be more evaluated on the clarity of the report rather than on its length. To train you to write professional research papers, you should use LaTeX in the ICML 2016 template format (download the template here). You may use appendices for additional details beyond 8 pages if you want, but be aware that as in standard conference reviewing, I might only read the first 8 pages (so the main content has to be there), and also, succinctness is more valued here than length!
A poster presentation of 6 minutes, from at most 68 lettersized pages (or as a poster format if you fancy it, but this is not required) which will be displayed on rolling boards during a poster session on Tuesday December 12th time and place TBD. The presentation (in French or English) is also geared towards other students and the goal is to highlight in the allocated time the salient points of your project. Like in a regular conference poster session, students are encouraged to attend other student posters. Other guidelines for the poster and presentation:
The poster should be in English so that all students can understand it (but your presentation to me can be in French if you prefer).
The content of your poster has a double purpose:
to explain clearly to other students of the class the model, problems and algorithms you have worked on, and any interesting observations that you have made.
to be the support of your 6 minutes oral presentation of your project
The 6 minutes timing will be strict as we want to be able to ask you a couple of questions in addition and there are many of you. We highly recommend that you prepare ahead of time what you will say during these 6 minutes. Highlight your understanding and the main things you have done (model, main algorithmic ideas, data, results).
IMPORTANT
Each group of students has to obtain the agreement of the teacher on their project by submitting a short description of it on Studium by November 7th.
Each student or group of students has to submit a small midproject report (one page pdf document) presenting the progress and the obtained results, so that some feedback may be given. This report has to submitted on Studium before November 28th.
The various steps are summarized below.
End of October  Choose a project (three or four students per project). 
Before 11/7  Choose your group and give your project choice on Studium. 
Before 11/28  Send a draft (1 page) + first results on Studium. 
On 12/12  Poster session 
Before 12/20  Submit your project report (48 pages, ICML format, on Studium) 
The goal of this list of projects is to show classical (and if possible interesting) articles using or improving graphical models. This may give you an idea of current research topics as well as applicative projects. Even if you don't select any of these, reading some of them is advised.
Probabilistics PCA  Interpretation of PCA as a graphical model
close to factorial analysis. A situation where EM has no local
minima. Tipping, M. E., Bishop, C. M. 1999. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B 61(3):611622. [pdf] 
Learning graph structure  multinomial
models 
For complete discrete data, learning of
parameters and directed acyclic graph. D. Heckerman, D. Geiger, D. Chickering. Learning Bayesian networks: The Combination of Knowledge and Statistical Data. Machine Learning, 20:197243, 1995. 
Learning graph structure  Gaussian models 
For complete Gaussian data, learning of
parameters and directed acyclic graph. D. Geiger, D. Heckerman. Learning Gaussian networks. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 235243. 
Variational methods for inference  Class of method for approximate inference. An introduction to variational methods for graphical models. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. In M. I. Jordan (Ed.), Learning in Graphical Models, Cambridge: MIT Press, 1999 Its application to Bayesian inference. Beal, M.J. and Ghahramani, Z. Variational Bayesian Learning of Directed Graphical Models with Hidden Variables To appear in Bayesian Analysis 1(4), 2006. 
Simulation methods for inference (particle
filtering) 
A simulation for dynamic graphical models Chapter from Kevin Murphy S. Arulampalam, S. Maskell, N. J. Gordon, and T. Clapp, A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking, IEEE Transactions of Signal Processing, Vol. 50(2), pages 174188, February 2002. Doucet A., Godsill S.J. and Andrieu C., "On sequential Monte Carlo sampling methods for Bayesian filtering," Statist. Comput., 10, 197208, 2000 
SemiMarkovian models  A class of models allowing to model the time
spent in any given state for a Markov Chain and an HMM. Note from Kevin Murphy [pdf] 
Learning parameters in an undirected graphical model (Markov random fields)  Chapter 9 of Mike's book and articles. 
Dynamic graphical models  Chapter from Kevin Murphy. Specific topics to be defined. 
General applications of the sumproduct algorithms (e.g., to the FFT)  The
generalized
distributive law, S. M. Aji, R. J. Mceliece Information Theory, IEEE Transactions on, Vol. 46, No. 2. (2000), pp. 325343. 
Independent Component Analysis  A. Hyvarinen, E. Oja (2000): Independent
Component Analysis: Algorithms and Application, Neural
Networks, 13(45):411430, 2000. Course of Herve LeBorgne: http://www.eeng.dcu.ie/~hlborgne/pub/th_chap3.pdf

Canonical Correlation Analysis 
CCA is analogous to PCA for the joint analysis of two random
vectors X and Y.

Clustering through a mixture of PCA 
M. E Tipping et C. M Bishop, Mixtures of probabilistic principal component analyzers, Neural computation 11, no. 2 (1999): 443482. 
Stochastic relational models 

Conditional Random Fields 
Charles Sutton, Andrew McCallum An Introduction to Conditional Random Fields for Relational Learning . In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning. MIT Press. 2007. 
Dirichlet Process 

Factorial HMM 
Z. Ghahramani et M. I Jordan, Factorial
hidden
Markov models, Machine
learning 29, no. 2 (1997): 245273 
Generalized PCA 
M. Collins, S. Dasgupta, et R. E
Schapire, A
generalization of principal component analysis to the exponential
family, Advances in neural
information processing systems 1 (2002): 617624. 
Structure learning by L1 regularization 
J. Friedman, T. Hastie, et R.
Tibshirani, Sparse
inverse
covariance estimation with the graphical lasso, Biostatistics
9, no. 3 (2008): 432. 
Mixture of logconcave densities 
Interesting nonparametric
models for unimodal distributions. distributions, Computational Statistics & Data Analysis 51, no. 12 (2007): 62426251. 
These articles present classical applications. They may give you ideas for an applicative project or may be used for article reviews.
Bioinformatics  Chapter 23 of Mike's book. Phylogenetic HMM: A. Siepel et D. Haussler, Phylogenetic hidden Markov models, Statistical methods in molecular evolution (2005), 3, 325351. 
Vision/Speech  Articles from Kevin Murphy: "Using the Forest to See the Trees:A Graphical Model Relating Features, Objects and Scenes" Kevin Murphy, Antonio Torralba, William Freeman. NIPS'03 (Neural Info. Processing Systems) Dynamic Bayesian Networks for AudioVisual Speech Recognition A. Nefian, L. Liang, X. Pi, X. Liu and K. Murphy. EURASIP, Journal of Applied Signal Processing, 11:115, 2002 Optimization for MAP inference in computer vision: MRF Optimization via Dual Decomposition: MessagePassing Revisited, Komodakis, Paragios, Tziritas, ICVV 2007. Longer technical report version 
Robotics  Automatic construction of maps Simultaneous Localization and Mapping with Sparse Extended Information Filters Thrun et al. The International Journal of Robotics Research.2004; (see also chapter 15 of Mike's book on Kalman filtering) 
Text  Naive Bayes: A. McCallum and K. Nigam. A comparison of event models for Naive Bayes text classification. In AAAI98 Workshop on Learning for Text Categorization, 1998. Latent Dirichlet allocation. D. Blei, A. Ng, and M. Jordan. Journal of Machine Learning Research, 3:9931022, January 2003. [.pdf  code] topic modeling webpage 
Text  Natural language processing  S. Vogel, H. Ney, and C. Tillmann.
HMMbased word alignment in statistical translation. In Proceedings
of the 16th conference on Computational linguistics, pp.
836841, Morristown, NJ, USA, 1996. Association for Computational
Linguistics. Non contextual probabilistic grammars: Notes de cours de CMU, 1999 
N most probable configurations  Implementation of an algorithm (HMM or more
complex graphs), from the following articles: Dennis Nilsson, Jacob Goldberger. An Efficient Algorithm for Sequentially finding the NBest List , IJCAI, 1999 Chen Yanover, Yair Weiss, Finding the M Most Probable Configurations Using Loopy Belief Propagation, NIPS 2003. 
Computation of treewidth  Comparing the classical heuristics and finer
methods: Mark Hopkins and Adnan Darwiche A Practical Relaxation of ConstantFactor Treewidth Approximation Algorithms Proceedings of the First European Workshop on Probabilistic Graphical Models 2002 Also some exact methods Stefan Arnborg, Derek G. Corneil, Andrzej Proskurowski, Complexity of finding embeddings in a ktree, SIAM Journal on Algebraic and Discrete Methods (1997) 
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