Recommended online courses in probabilistic modeling:

  • MIT AI Lab course webpage. See especially Tommi Jaakkola's course on machine learning and the related handouts.

  • Professor Coolen's collection of courses on Information Theory in ANNs, Stat Mechanics of ANNs and some topics on machine learning, including Bayesian learning, Gaussian Processes, and SVMs. Taught in 1997-2002 in King's College London. (Thanks to David Barber for this reference).

  • Here is the Gatsby resource page for unsupervised learning, and its more recent version (Fall 2001). Features introduction to unsupervised learning, linear dynamical models, sampling and variational inference in BNs and some relevant matlab code.

  • Chris Williams 's courses in Probabilistic Modeling and Reasoning and Learning from Data in Edinburgh. Excellent for getting into the field and starting to love it.

  • A course in Statistical Physics in Auckland's Physics Department.

  • A course in Data Mining at PennState with links to recent work on spectral clustering.

  • Michael Kearn's courses in computational game theory and machine learning.

  • An excellent page on Game Theory.

  • Some AI books online:
    You can download online all of the following AI and AI-related books.

    Learning in Graphical Models [Ed. Jordan, MIT Press, '98]
    Some papers from Jordan's collection on inference, independence, and learning in graphical models (with links to contributors).

    Handbook of Applied Cryptography [A.J. Menezes, P.C. van Oorschot and S.A. Vanstone, CRC Press, '01]
    (Thanks to David Barber for pointing this link to me)

    Learning with Kernels SVMs, Regularization, Optimization and Beyond [B. Schoelkopf, A. Smola, MIT Press, '02]
    (Selected chapters available online)

    Machine Learning, Neural and Statistical Classification [D. Michie, D. Spiegelhalter, C. Taylor (ed), Ellis Horwood '94]

    Information Theory, Inference and Learning Algorithms [D. MacKay, Cambridge University, '03]

    Convex Optimization [Stephen Boyd and Lieven Vandenberghe, Stanford University, '01]

    Probability Theory: the Logic of Science [E. T. Jaynes, Washington University, fragmentary edition of '94]

    Numerical Recipes in C [Press et. al., Cambridge University Press, '93]

    Reinforcement Learning: An Introduction [R.S. Sutton and A.G. Barto, MIT Press, '98]

    AI and Molecular Biology [Ed. by L. Hunter]

    Steven Shreve's Lectures on Stochastic Calculus and Finance [P. Chalasani and S. Jha, CMU, '97]

    The Scientist and Engineer's Guide to Digital Signal Processing [S. W. Smith, California Technical Publishing, '98]

    Finally, what I consider to be THE Text on Learning - J. London's "Martin Eden" .

    Other Pointers to AI Books:

    General Sources: Math Books Online, The Universal Library

  • Here is a list of book announcements on pattern recognition (updated daily).

  • Warren Sarle offers what I find to be quite a good review of books focusing on ANNs in his Neural Network FAQ.

  • Kevin Murphy offers a useful list of recommended reading on BNs with pointers to some online sources.
    More useful links to books, online papers, reviews, and tutorials on probabilistic modeling, uncertainty, and AI are here.

  • Michael Jordan is writing "An Introduction to Graphical Models" (with guest chapters by Chris Bishop). I believe it is going to be one of the best and most comprehensive introductory book on graphical models. The drafts are no longer available online, but the book is to be published soon.

  • The Historical Math Book Collection at Cornell.
  • The Universal Library (browse the online catalog).

    AI Groups in the UK:

    I get a growing number of requests from recent graduates and last-year undergraduate students who ask me to help them find information about studying Artificial Intelligence in the UK, and scholarships, financial aid, etc. in Edinburgh.

    First, you might want to have a look at the UoE's School of Informatics. Also, here is my brief list of UK institutions offering courses and degrees in AI-related fields and PhD/Master funding. (For a list of American institutions see Stuart Russel's links below).

    AI Links:
    Here are some links I find most useful.

  • PH Home Page : A - The Pattern Recognition group at Delft University of Technology.
    See especially the Pattern Recognition Files and the Report services
  • ResearchIndex has links to over 300,000 papers available online.
  • AUAI (Association for Uncertainty in Artificial Intelligence) - news, papers, tutorials, people, some very useful links.
  • Gatsby Computational Neuroscience Unit and the PoE Group.
  • NIPS (Neural Information Processing Systems) events and some past papers.
    All NIPS Proceedings are currently available online!
  • The Kalman Filter webpage has some relevant links, papers, tutorials, and code.
  • Kernel Machines page has a lot of useful stuff on SVMs.
  • David Aha's Machine Learning Page (for introduction to ML see the course on Machine Learning at MIT).
  • Thomas Minka's superb Statistical Glossary.
  • Kevin Murphy's statistical Matlab software and overview of developments in Bayesian nets.
  • MCMC Preprint Service has a list of all registered papers on Markov Chain Monte Carlo methods (many available online). For introduction to MCMC see Radford Neal's excellent MCMC tutorial.
  • Russel's AI Links (people, research groups, companies).
  • MathWorld is back online!!
  • JSTOR collection of online journals starting with the first issues (as early as 1800's) and ending within a 2-5 year gap from now. (Open only to participants. See the list of participating intstitutions outside UK and US).
  • Other AI links, also quite useful but only partially sorted and graded are here.

    Finally, some people working at Machine Learning (the alphabetical list is far from being complete): David Barber, Chris Bishop, Geoffrey Hinton, Tommi Jaakkola, Michael Jordan, Michael Kearns, David MacKay, Thomas Minka, Kevin Murphy, Chris Williams.

    The rotating news are from the UCB's Department of Statistics.

    Page last updated: Sep. 28, 2010

    This page is maintained by Felix Agakov