+DS vLE: 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.
This session is part of the Duke+DataScience (+DS) program virtual learning experiences (vLEs). To learn more, please visit https://plus.datascience.duke.edu