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Gaussian Processes for Machine Learning

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If you have a question about this talk, please contact Zoubin Ghahramani.

Advanced Machine Learning Tutorial Lecture

A Gaussian process (GP) model is a Bayesian probabilistic model for nonlinear regression. As such, it can be useful to any data modeller interested in making predictions from noisy data, together with uncertainty estimates. Although at its core a regression model, the GP can be built upon to be used in a wide range of applications. I will discuss a variety of these, from classification tasks to human pose modelling. Other topics I will discuss are the design of covariance functions for different tasks, and the recent development of sparse GP approximations to handle large data sets.

This talk is part of the Machine Learning @ CUED series.

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