In this master class we will give a short introduction to Gaussian process models, and then explore their use in the domain of Bayesian Optimization. Gaussian process models are flexible models which allow us to place probability distributions over functions. In Bayesian Optimization, the Gaussian process is used as a surrogate for the process of interest. Rather than directly optimizing the process, the surrogate is optimized. This leads to an efficient approach for improving efficiency in a wide range of physical systems. The seminar will introduce lab classes which will make use of the python software GPy and GPyOpt (https://github.com/sheffieldml/GPy, https://github.com/sheffieldml/GPyOpt). 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.
For more details of this talk and the school schedule see here.