Welcome to the lab for the Gaussian process section at the Machine Learning Summer School in Sydney.
This notebook provides you with the guide to your lab classes for Gaussian processes. The lab classes are intended to help get you familiar with modeling with Gaussian processes as
The lab classes are based on our two software packages,
pods which is used for access to datasets and
GPy (release 21st November 2014) for Gaussian processes. You can install the GPy framework with
pip install GPy
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
on some systems you may need to use
pip install -pre pods to allow the prerelease to install.
As well as these lab classes here are a range of tutorials on how to use
GPy, many of which are written by members of the Sheffield research group.
GPy is under active development and is released under a BSD license, you'd also be very welcome to contribute!
Before you start, if you aren't familiar with probabilistic processes, the following lab classes from the GPRS schools might be useful. The first session will allow you to become familiar with the Jupyter (the ipython notebook) and start to work with Gaussian processes.
JupyterA quick introduction to
The session 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.
Things we haven't had time to cover in the MLSS can be found below.
Gaussian processes for learning vector valued functions.
These examples look at approximations for speeding up inference in Gaussian processes and/or making inference tractable.
These examples look at dimensionality reduction with Gaussian processes.