Background for "Structure discovery in nonparametric regression through compositional kernel search"

Created by: Roger Grosse
Intended for: machine learning researchers

This roadmap gives the background for my ICML 2013 paper, "Structure discovery in nonparametric regression through compositional kernel search." This covers the basic machine learning concepts that the paper depends on, and should be sufficient for understanding it at a conceptual level.

The paper focuses in particular on learning the structure of Gaussian processes. In particular, you'll want to be familiar with:

To control for the complexity of the kernel, we use the Bayesian information criterion (BIC).