Gaussian processes (GPs) are a challenging area of Bayesian machine learning to get started with – from wrapping your head around dealing with infinite dimensional Gaussian distributions, to understanding kernel functions and how to choose the right one for the right task, all on top of having solid knowledge of Bayesian inference.
While primarily used as a powerful regression model with the ability to estimate uncertainty in predictions, GPs can also be used for classification, and have a very wide range of applications.
These are some of the resources I have used, or are planning to use in my on-going process of learning about GPs.
