# Gaussian process classification

(30 minutes to learn)

## Summary

Gaussian process classification is a Bayesian model for nonparametric classification. The data points have associated latent variables which are drawn from a GP prior, and the labels are modeled as stochastic functions of the latent variables.

## Context

This concept has the prerequisites:

- Gaussian process regression (Gaussian process classification builds on Gaussian process regression.)
- Bayesian logistic regression (Gaussian process classification is a kernelized version of Bayesian logistic regression.)

## Core resources (read/watch one of the following)

## -Free-

→ Gaussian Processes for Machine Learning

A graduate-level machine learning textbook focusing on Gaussian processes.

Location:
Section 3.3, pages 39-41

## -Paid-

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 6.4.5, pages 313-315

## See also

- We can approximate the solution to GP classification using the Laplace approximation .
- Gaussian processes have a variety of uses in machine learning, including:
- regression
- black-box optimization (where we only get to evaluate the function, and doing so is expensive)
- reinforcement learning