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3rd VIPS Advanced School on
Computer Vision, Pattern Recognition
and Image Processing

NEWS: 6 December 2004

  • Prof. Figueiredo has given us some new material! >>here<<

  • NEWS: 23 November 2004

  • The school is now over and it's been a great success! We wish to thank you prof. Figueiredo for being a great host, and all the people who came to attend to our third school. Thank you all for the wonderful experience, you made all our efforts worthwhile!
  • Click >>here<< to access all the materials of this course as well as photographs of the attendants.

  • NEWS: 10 October 2004

  • Checkin at 10 am (one hour before course beginning)
  • Click >>here<< for the final lectures schedule

  • Click here for a tourist tour!
    November 15-19, 2004
    Organized by the Vision, Image Processing and Sound Laboratory Department of Computer Science, University of Verona, Italy

    This is the 3rd Advanced School organized by the VIPS laboratory, the third of a series of advanced lectures on significant topics in Computer Vision, Pattern Recognition, and Image Processing.

    These courses are particularly addressed to PhD students, but open to all types of researchers. Each course will typically last one week and will be focused on one specific topic in order to provide a more productive interaction with the lecturer.

    The maximum number of participants is limited to 50 persons. In case of a larger number of applications, priority will be given to PhD students.

    This school is titled "Introduction to Bayesian Inference and Statistical Learning".


    Vittorio Murino
    Andrea Fusiello

    Local Organizers

    Andrea Colombari
    Marco Cristani
    Michela Farenzena
    Cheng Dong Seon


    Prof. Mario A. T. Figueiredo

    Department of Electrical and Computer Engineering
    Instituto Superior TÚcnico (IST), Lisbon, Portugal

    Course title

    Introduction to Bayesian Inference and Statistical Learning


    1. Introduction to Bayesian decision theory.
    Likelihoods and priors, Bayes rule, posterior expected loss, Bayes-optimal decision rules (e.g., MAP, posterior mean, posterior median, etc...), conjugate priors and exponential families, sufficient statistics, non-informative priors, compound inference, hierarchical models.

    2. Estimation with missing data.
    The EM algorithm, definition, monotonicity, convergence, special case of exponential families, and application examples (e.g. mixture models, robust inference, heavy tailed priors).

    3. Linear regression.
    Least squares and maximum likelihood regression, unbiasedness and minimum variance, bias/variance decomposition, ridge regression, degrees of freedom, LASSO regression, variable selection, bridge regression, Bayesian interpretations, algorithms.

    4. Linear classification
    Generative versus discriminative approaches, logistic regression and its generative interpretation, algorithms for logistic regression, linear Fisher discriminants.

    5. Model assessment and selection.
    The bias/variance decomposition, in-sample error estimates, Mallow's Cp criterion, Akaike's information criterion (AIC), Bayesian information criterion (BIC), minimum description length (MDL).

    6. Introduction to support vector machines.
    Kernels, inner products, and Mercer's theorem; support vector machines for classification of separable and non-separable data, support vector regression.

    7. Introduction Bagging, Boosting, and other ensemble methods.
    Additive models and trees, forward stagewise additive models, exponential loss.

    Final Lectures Schedule

    Monday 15 Tuesday 16 Wednesday 17 Thursday 18 Friday 19
    11 - 13 10 - 13 10 - 13 10 - 13 10 - 13
    15 - 17 15 - 17 15 - 17 15 - 17 No Class

    Course Fees

    150 euro for PhD and undergraduate students.
    200 euro for post doc, researchers, and other people working directly in a university.
    300 euro for everybody else.


    If you are interested, you must send an email to in which you ask for participation. Please, state your identity and your status (undergraduate, PhD student, other) and wait for the confirmation email. The ultimate deadline is October 18, 2004.

    Attached to our confirmation email you will find a registration form to print, compile and send together with a proof of the payment by fax before October 25, 2004, to the following no. +39 045 8027068, to the attention of Prof. V. Murino, 3nd VIPS School on Computer Vision, Pattern Recognition, and Image Processing.

    The proposed payment method is bank wire transfer (all necessary data are in the form).

    Important Dates

    Registration deadline:

    October 18, 2004


    Course Fee payment deadline:

    October 25, 2004

    (Registration form + Proof of payment)


    November 15-19, 2004


    The accomodation costs are not covered by the Course Fee. However, we have made agreements with some convenient hotels and you can find a list of available places here.
    If you wish to take advantage of these opportunities please remember to notify to the hotel that you are attending our school.

    Information on how to reach our department are presented in this page.

    For any other information, please send an email to

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    Last revision: September 15, 2004