Draft Schedule

Room: Lecture A54, PSC

9:00-10:30 Introduction to Gaussian Processes [slides]

11:00-11:30 Coffee Break

11:30-13:00 Covariance Functions and the Marginal Likelihood [slides]

13:00-14:00 Lunch

14:00-16:00 Lab Class 1: Gaussian Processes [jupyter notebook]

16:30-17:30 Latent Variable Models with Gaussian Processes [slides]

Abstracts

Introduction to Gaussian Processes

Neil Lawrence

This first talk will be an introduction to Gaussian process models that will assume knowledge of probability, linear algebra and the multivariate Gaussian.

Covariance Functions and the Marginal Likelihood

Neil Lawrence

This talk will develop the idea of the covariance function and give intutions as to how the marginal likelihood can be maximized. Given time we willl also develop the idea of multiple output Gaussian process models.

Latent Variable Models with Gaussian Processes

Neil Lawrence

Gaussian process models are flexible non parametric probabilistic models for functions. In this talk we will show how they can be incorporated into latent variable models to form probabilistic latent variable models. The resulting approaches have some unusual properties. In particular, they express conditional independencies across features, rather than data. This implies that rather than a curse of dimensionality they exhibit a blessing of dimensionality. We will give background of the model and show some exemplar applications.