Instructor:  Milos Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos_at_cs_pitt_edu
office hours: TBA

Basics of Matrix algebra

• Basic concepts in probability, algebra, math
• Introduction to matrices
• Learning from Data: Mathematics Primer
• Mike Brookes The Matrix Reference Manual

• CS2750 Lecture notes
• Chris Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
• R.O. Duda, P.E. Hart, D.G. Stork. Pattern Classification. Second edition. John Wiley and Sons, 2000.

Complexity Results for Bayesian Belief networks

• Cooper, G.F., The Computational Complexity of Probabilistic Inference using Bayesian Belief Networks, Artificial Intell., 42, pp. 393-405, 1990.
• Dagum, P. and Luby,M.(1993). Approximating probabilistic inference in bayesian belief networks is np-hard. Artificial Intelligence, 60:141--153.

Exact inference algorithms

• D. Koller and N. Friedman. Probabilistic graphical models, 2009.
• C. Huang, A. Darwiche. Inference in Belief Networks: A Procedural Guide.
• M. Jordan. Graphical models. Chapter 16. (see CS 2750 web page from Spring 02)
• Peter Lucas. Pearl's algorithms Lecture notes

Monte Carlo methods:

• D. Koller and N. Friedman: Probabilistic Graphical Models, 2009
• Rubinstein. Simulation and the Monte Carlo Method. 1981.
• David MacKay. Introduction to Monte Carlo methods: forward, rejection sampling, importance sampling
• Andrieu et al. An introduction to MCMC for Machine Learning. Machine Learning, vol. 50, pp.5-43, 2003.

Loopy Belief propagation

• Kevin P. Murphy, Yair Weiss, Michael I. Jordan. Loopy Belief Propagation for Approximate Inference: An Empirical Study., 1999.
• Jonathan S. Yedidia Generalized Belief Propagation. 2000.
• Jonathan S. Yedidia, William T. Freeman, Yair Weiss Constructing Free Energy Approximations and Generalized Belief Propagation Algorithms, 2002.

Basics:

• Lecture notes for CS2750
• Wray Buntine. A Guide to the Literature on Learning Probabilistic Networks From Data. 1996.
• David Heckerman A Tutorial on Learning With Bayesian Networks 1996.
• Wray Buntine. Operations for Learning with Graphical Models JAIR, 1994.

EM

• Lecture notes for CS2750
• A.P. Dempster, N.M. Laird, D.B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal statistical society, vol. 39, issue 1, pp. 1-28, 1977.
• Expectation-maximization in M. Jordan, C. Bishop. Introduction to graphical models.

Applications of PCA and SVD basics:

• Lecture notes for CS2750
• Chris Bishop. Chapter 12.1.
• Hastie, Tibshirani and Friedman. Elements of statistical learning. Section 14.5
• Tutorial on PCA and SVD
• Tutorial on PCA
• Hopcroft, Kannan. A book chapter on SVD

Applications of PCA:

• Information Retrieval.
  • Michael W. Berry, Zlatko Drmac, Elizabeth R. Jessup. Matrices, Vector Spaces, and Information Retrieval, SIAM Review, 1999.
• Link analysis.
  • Borodin et al. Finding Authorities and Hubs From Link Structures on the World Wide Web
  • Jon M. Kleinberg. Authoritative Sources in a Hyperlinked Environment, Journal of ACM. 1999.
  • Sergey Brin, Lawrence Page The Anatomy of a Large-Scale Hypertextual Web Search Engine
  • A. Ng. Stable Algorithms for Link Analysis

Latent Variable Models for text analysis, information retrieval and link analysis: PLSA, LDA

Probabilistic latent semantic analysis (LSA)

• Thomas Hoffman. Probabilistic Latent Semantic Analysis. UAI-99, 1999.
• Thomas Hofmann. Probabilistic Latent Semantic Indexing. SIGIR-99, 1999.

pLSA for link analysis

• David Cohn and Huan Chang. Learning to probabilistically identify Authoritative documents
• David Cohn and Thomas Hoffman. The Missing Link - A Probabilistic Model of Document Content and Hypertext Connectivity

Latent Dirichlet Allocation

• David M. Blei, Andrew Y. Ng, Michael I. Jordan. Latent Dirichlet Allocation. JMLR, 2003.

Latent Variable Models for PCA

EM algorithms for probabilistic PCA

• Michael E. Tipping, Chris M. Bishop. Probabilistic Principal Component Analysis, 1999. (TR in 1997)
• Sam Roweis. EM Algorithms for PCA and SPCA. NIPS-1998.
• Tipping and Bishop 1998 Mixtures of Probabilistic Principal Component Analysers

Extensions of probabilistic PCA

• Wray Buntine and Sami Perttu. Is multinomial PCA multi-faceted clustering or dimensionality reduction AI in statistics, 2003.
• Michael Collins, Sanjoy Dasgupta, Robert E. Schapire. A Generalization of Principal Component Analysis to the Exponential Family

Latent variable component analysis

Variational methods:

• Jordan et al An Introduction to Variational Methods for Graphical Models , 1999.
Z. Ghahramani. Variational methods (lecture notes)
• Tommi Jaakkola Tutorial on variational approximation methods

Variational ML learning for component analysis

• Z. Ghahramani. Factorial Learning and the EM Algorithm 1996.
• A.P. Dunmur, D.M. Titterington (1997). On a Modification to the Mean Field EM Algorithm in Factorial Learning
• Ghahramani and Beal (2000). Graphical Models and Variational Methods
• Ghahramani and Beal Variational Inference for Bayesian Mixture of Factor Analyzers , 2000.
• X. Lu, M. Hauskrecht, R.S. Day. Variational Bayesian learning of the cooperative vector quantizer model. Part I: The Theory. Technical Report, Center for Biomedical Informatics, CBMI-02-181, 2002.

Probabilistic models for time-series and sequence analysis

• TBA

Conditional Random Fields

• Andrew McCallum. Introduction to Conditional Random Fields

  Laplacian Eigenmaps for dimensionality reduction

  • Tenenbaum, De Silva, Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction, 2000
  • Roweis, Saul. Locally Linear Embedding, 2000 .
  • Belkin, Niyogi. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, 2002 .

  Spectral clustering

  • von Luxburg Tutorial on Spectral Clustering .
  • C. Ding. Spectral clustering Tutorial. presented at NIPS 2004.
  • Andrew Y. Ng, Michael I. Jordan, Yair Weiss. On Spectral Clustering: Analysis and an algorithm.

  Diffusion kernels

  • Diffusion maps:
    • Lafon, Lee Diffusion maps .
    • Kondor, Lafferty Diffusion kernels on Graphs
    • John Lafferty, Guy Lebanon. Diffusion Kernels on Statistical Manifolds.
  • Resistance kernel:
    • Effective resistance

  Semi-supervised learning, label propagation

  • Zhu, Ghahramani Learning from labeled and unlabeled data
  • Zhu, Ghahramani, Lafferty Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
  • Zhou at al Learning with Local and Global Consistency
  • Zhu et al Semi-supervised learning

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  Last updated by milos on 10/20/2014