Labs

All of the lab sheets are written in Python 3 given in Jupyter notebook format.

Each lab sheet will be made available on the day of the lab.

Lab 1: Gaussian Process Regression

This lab is designed to introduce Gaussian processes in a practical way, illustrating the concepts introduced in the first two lectures. The key aspects of Gaussian process regression are covered: the covariance function (aka kernels); sampling a Gaussian process; and the regression model. The notebook will introduce the open source Python library GPy which handles the kernels, regression and optimisation of hyperparameter, allowing us to easily access the results we want.

   

Lab 2: Global Optimisation with Gaussian Processes

This lab introduces the basic concepts of Bayesian optimisation with BoTorch. The student will build and compare different models and acquisition functions to solve several optimisation problems.

   

Other Labs

Arthur Leroy’s group has developed several tutorials on multi-output GPs.

Pablo M Olmos will use several notebooks for his two lectures in the Summer School.