University of Cambridge > Talks.cam > Machine Learning @ CUED > Gaussian Processes for Machine Learning

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Gaussian Processes for Machine Learning

Download to your calendar using vCal

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.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Average on weekdays: 8806.60416667 kWh Wolfson Lecture Theatre Zoom link: https://maths-cam-ac-uk.zoom.us/j/98016675669

Other talks

Group Work Title to be confirmed Loke CTR Seminar Series - Vincent Pasque Title TBC Title and abstract tba The United States of America at 250: Reflections on the Past, Present and Future of Independence
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity