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Introduction to Gaussian processes for Machine Learning


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In this session, Prof.  Álvarez will define a Gaussian process (GP) model and describe how it is used to tackle (non-linear) regression problems including defining the kernel function, the key function that defines the Gaussian process. He will define how we can use optimization of the marginal likelihood to estimate (hyper-)parameters in the GP model, and (time permitting) how GPs are used for pattern classification, multiple-output regression, unsupervised learning and Bayesian optimization.

Mauricio A Álvarez, PhD. is an Associate Professor in the Department of Computer Science at The University of Sheffield in the United Kingdom.

On the day before the session, all registrants will receive an e-mail with a link and meeting information.

Please register at least 15 minutes prior to the start of the session to allow time for the system to generate a personalized e-mail with the access link.

If you are not a member of the Duke community, you can register directly through Zoom here: https://duke.zoom.us/webinar/register/WN_KE7I-sLuQNmpJHnR_BcLig


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Status Open
Date Thursday, November 18th, 2021
Time 11:00am - 12:00pm
Location Virtual Classroom
Leader Mauricio Álvarez
Enrolled 5