# GP classification with the Laplace approximation

(1.4 hours to learn)

## Summary

Unlike with GP regression, there is no closed-form solution to GP classification. The most basic method for approximating it is to use the Laplace approximation, thereby formulating it as an optimization problem.

## Context

This concept has the prerequisites:

- Gaussian process classification
- the Laplace approximation
- fitting logistic regression with iterative reweighted least squares (We can optimize the objective function using the same IRLS method as in logistic regression.)
- learning GP hyperparameters (Part of fitting a GP classification model is learning the hyperparameters.)

## 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 3.4, pages 41-48

## Supplemental resources (the following are optional, but you may find them useful)

## -Free-

→ Bayesian Reasoning and Machine Learning

A textbook for a graudate machine learning course.

## -Paid-

→ Machine Learning: a Probabilistic Perspective

A very comprehensive graudate-level machine learning textbook.

Location:
Section 15.3.1, pages 525-528

→ Pattern Recognition and Machine Learning

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

Location:
Section 6.4.6, pages 315-318

## See also

-No Additional Notes-