  This document serves to complement our website which was developed with the
aim of exposing the students to Gaussian Processes (GPs). GPs are
non-parametric Bayesian regression models that are largely used by
statisticians and geospatial data scientists for modeling spatial data. Several
open source libraries spanning from Matlab [1], Python [2], R [3] etc., are
already available for simple plug-and-use. The objective of this handout and in
turn the website was to allow the users to develop stand-alone GPs in Python by
relying on minimal external dependencies. To this end, we only use the default
python modules and assist the users in developing their own GPs from scratch
giving them an in-depth knowledge of what goes on under the hood. The module
covers GP inference using maximum likelihood estimation (MLE) and gives
examples of 1D (dummy) spatial data.
