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+DS vLE: Mauricio Alvarez: Introduction to Gaussian processes for Machine Learning
In this session, Prof. Mauricio 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.

Nov 18, 2021 11:00 AM in Eastern Time (US and Canada)

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