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Gaussian processes for machine learning

Thursday 9th August 2007 - 14:00 to 15:00
INI Seminar Room 1

The aim of this talk is to give an overview of the work that has been going on in the Machine Learning community with respect to Gaussian process prediction; this may be of particular interest to statisticians who are less familiar with the machine learning literature.

Particular topics to be covered include approximations for inference (e.g. expectation propagation), covariance functions, dealing with hyperparameters, theoretical viewpoints, and approximations for large datasets.

Related Links

University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons