Welcome to the Gaussian process summer school in Sydney, Australia.
This notebook provides you with the guide to your lab classes for the entire school. The lab classes are intended to help get you familiar with modeling with Gaussian processes as well as the principles of probabilistic inference.
The lab classes are based on our GPy software, the most recent release was on 21st November 2014. You can install the GPy framework with
pip install GPy
As well as these lab classes there are a range of tutorials on how to use
GPy, many of which are written by members of my research group.
GPy is under active development and is released under a BSD license, you'd also be very welcome to contribute!
As well as the GPy software we use our
pods software for 'open data science' for access to data sets and other resources.
pip install -pre pods
We will assume background in probabilistic modeling and some familiarity with python and IPython notebook, or Jupyter as it is now known. If you would like to review this material, you can do so with the lab classes below and associated videos.
JupyterA quick introduction to
The first day will focus on Gaussian process models and developing covariance functions.
GPyis a Python Gaussian process framework that implements many of the ideas we'll see in the course. In this session we introduce its covariance functions and sample from the associated Gaussian processes.
The second day reviews multiple output Gaussian processes for learning vector valued functions and approximations for Gaussian processes.
These examples look at latent variable models and approximations for speeding up inference in Gaussian processes and/or making inference tractable.