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

Basics

Matrices, Algebra

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

Density estimation

• CS2750 Lecture notes
• Chris Bishop. Pattern Recognition and Machine Learning. Springer, 2006.

Grahical models: Bayesian Belief Networks, Markov Random Fields, Conversion of BBNs to MRFs, Clique trees (tree decompositions of the MRFs)

• Chris Bishop. Chapter 8. Pattern Recognition and Machine Learning. Springer, 2006.

Exact inference algorithms: factor graphs

• Chris Bishop. Chapter 8. Pattern Recognition and Machine Learning. Springer, 2006.
• Kschischang, Frey, Loeliger. Factor Graphs and the Sum-Product Algorithm, 1998.

Exact inference algorithms on clique trees

• Lecture notes on clique trees by A. McCallum.
• Stefano Ermon's lecture notes on Junction/Clique trees

Monte Carlo methods:

• C. Bishop textbook. Chapter 11.
• 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.

Variational methods:

• C. Bishop textbook. Chapter 10.
• 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

Loopy Belief propagation (not covered in Fall 2018)

• 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.

Learning BBNs:

• 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.

Learning MRFs:

• Bishop. Pattern Recognition and Machine Learning. Chapter 20.
• Z. Ghahramani. Learning MRFs
• Jirousek, Kushmerick. Constructing probabilistic models
• Lecture notes on Learning MRFs by Sam Roweis

PCA:

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

Autoencoders

• Chapter 14. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.

SVD:

• Tutorial on PCA and SVD
• Hopcroft, Kannan. A book chapter on SVD

Applications of PCA/SVD:

• 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

Non-negative matrix factorization:

Latent Variable Component Analysis Models

Probabilistic PCA

• C. Bishop. Pattern recognition and Machine Learning. Chapter 12.2.
• Michael E. Tipping, Chris M. Bishop. Probabilistic Principal Component Analysis, 1999. (TR in 1997)
• Sam Roweis. EM Algorithms for PCA and SPCA. NIPS-1998.
• (not for Fall 2018) Tipping and Bishop 1998 Mixtures of Probabilistic Principal Component Analysers

Other latent variable models

• Z. Ghahramani Factorial Learning and the EM Algorithm
• X. Lu, M. Hauskrecht, R.S. Day. Variational Bayesian learning of the cooperative vector quantizer (CVQ) model. Part I: The Theory. , 2002
• Singliar, Hauskrecht. Noisy-or Component Analysis and its Application to Link Analysis. 2006
• 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
• Kingma, Welling. Auto-Encoding Variational Bayes

Non-linear dimensionality reduction and kernels

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 .

Kernel PCA

• Scholkopf, Smola, Muller Nonlinear Component Analysis as a Kernel Eigenvalue Problem

Diffusion kernels (not covered in 2018)

• 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

Models for document analysis, information retrieval and link analysis

Probabilistic latent semantic analysis (pLSA)

• 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.

Word and word similarity models: word2vec, CBOW, graph-based models

Semi-supervised learning, label propagation (not covered in Fall 2018)

• 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

Gaussian Processes

• Rassmussen, Williams. Gaussian Processes for Machine Learning.

Time series and sequence analysis models

Markov models, Hidden Markov models, Dynamic Belief networks

• Chris Bishop. Pattern Recognition and Machine Learning. Chapter 13.1-2.
• Russell and Norvig. Artificial Intelligence. Modern Approach. Chapter 15. Probabilistic reasoning over time.

Linear Dynamical model

• Chris Bishop. Pattern Recognition and Machine Learning. Chapter 13.3.
• Russell and Norvig. Artificial Intelligence. Modern Approach. Chapter 15.4. Probabilistic reasoning over time.

Approximate inference in Dynamic belief networks: Particle filtering

• Russell and Norvig. Artificial Intelligence. Modern Approach. Chapter 15.5-6. Probabilistic reasoning over time.

Autoregressive models (first and k-order AR, ARMA, ARIMA)

• TBA

Deep learning

Recurrent Neural Networks, LSTM

• Chapter 10. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.

Convolutional neural networks

• Chapter 9. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.

Deep generative models

• Chapter 20. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.

Generative Adversarial Networks

• Goodfellow et al. Generative Adversarial Nets.

Deep learning: Applications

• Chapter 12. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.

Tensors

• Porat. A Gentle Introduction to Tensors.
• Clarke. A Primer on Tensor Calculus