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

• Milos Hauskrecht. Basic Math for ML
• Milos Hauskrecht. Basics of probabilities for ML
• Basic concepts in probability, algebra, math
• Roweis. Introduction to matrices
• Learning from Data: Mathematics Primer
• Mike Brookes The Matrix Reference Manual

Grahical models: Bayesian Belief Networks, Markov Random Fields: introductory text

• Readings:
  • Chris Bishop. Chapter 8. Pattern Recognition and Machine Learning. Springer, 2006.
  • M. Jordan's Graphical models handouts:
    • Chapter 2. BBNs and MRFs and their relation.
    • Chapter 3. Variable elimination.
    • Chapter 4. Inferences.
  • Videos:
    • BBNs
    • Markov Random Fields

  BBNs and MRFs conversions

  • Lecture notes on clique trees by A. McCallum.

  Exact inference algorithms

  • Kschischang, Frey, Loeliger. Factor Graphs and the Sum-Product Algorithm, 1998.
  • Stefano Ermon's lecture notes on Graphical model inferences

  Approximate inference: 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.

  Approximate inferences: Variational methods:

  • Readings:
    • 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
  • Videos:
    • Variational EM

  Learning graphical models from data (not covered in Fall 2020)

  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

  Data representation, generative models of data

  Dimensionality reduction: PCA, Auto-encoders

  • Lecture notes for CS2750
  • Chris Bishop. Chapter 12.1.
  • Tutorial on PCA
  • Chapter 14. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.

  Probabilistic latent variable models: Probabilistic PCA, Factor analysis, CVQ, NOCA

  • 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.
  • 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.

  Modern deep generative models: Restricted Boltzman machines, Variational autoencoders

  • Readings:
    • Chapter 20. In: Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning.
    • Kingma, Welling. An Introduction to Variational Autoencoders, 2019
    • Kingma, Welling. Auto-Encoding Variational Bayes
  • Videos:
    • Variational autoencoders at 20:50 min

  Generative Adversarial Networks

  • Readings:
    • Goodfellow et al. Generative Adversarial Nets.
  • Videos:
    • GANs at 52:00 min

  Models for document analysis, information retrieval and link analysis

  Singular value decomposition (SVD), Applications to: LSI, link analysis

  • Tutorial on PCA and SVD
  • Hopcroft, Kannan. A book chapter on SVD
  • Application to Information Retrieval.
    • Michael W. Berry, Zlatko Drmac, Elizabeth R. Jessup. Matrices, Vector Spaces, and Information Retrieval, SIAM Review, 1999.
  • Application to 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

  Probabilistic latent semantic analysis (pLSA), Latent Dirichlet Allocation (LDA)

  • Thomas Hoffman. Probabilistic Latent Semantic Analysis. UAI-99, 1999.
  • Thomas Hofmann. Probabilistic Latent Semantic Indexing. SIGIR-99, 1999.
  • 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
  • David M. Blei, Andrew Y. Ng, Michael I. Jordan. Latent Dirichlet Allocation. JMLR, 2003.

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

  Time series and sequence models

  Discrete-state probabilistic 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.

  Continuous-state probabilistic models: Autoregressive models, Linear Dynamical model (LDM)

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

  Modern autoregressive models: Recurrent neural networks (LSTMs, GRUs)

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

  Topics in deep learning

  Self-attention mechanisms, transformers and all that <\b>

  Convolutional neural networks

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

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  Last updated by milos on 01/07/2020