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SUMMARY:Gaussian Processes for Machine Learning - Ed Snelson\, Gatsby Unit
 \, UCL
DTSTART:20061026T150000Z
DTEND:20061026T170000Z
UID:TALK5540@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:A Gaussian process (GP) model is a Bayesian probabilistic mode
 l for nonlinear regression. As such\, it can be useful to any data modelle
 r interested in making predictions from noisy data\, together with uncerta
 inty estimates. Although at its core a regression model\, the GP can be bu
 ilt upon to be used in a wide range of applications. I will discuss a vari
 ety of these\, from classification tasks to human pose modelling. Other to
 pics I will discuss are the design of covariance functions for different t
 asks\, and the recent development of sparse GP approximations to handle la
 rge data sets.
LOCATION:LR4\, Engineering\, Department of
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