We review the math and code needed to fit a Gaussian Process (GP) regressor to data. We conclude with a demo of a popular application, fast function minimization through GP-guided search. The gif below illustrates this approach in action — the red points are samples from the hidden red curve. Using these samples, we attempt to leverage GPs to find the curve’s minimum as fast as possible.

Appendices contain quick reviews on (i) the GP regressor posterior derivation, (ii) SKLearn’s GP implementation, and (iii) GP classifiers.