Gaussian processes for machine learning
Seminar Room 1, Newton Institute
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.
- http://www.gaussianprocess.org/gpml/ - Gaussian Processes for Machine Learning book website