Gaussian Processes

Gaussian Processes (GPs) are a non-parametric Bayesian machine learning technique used for regression and classification tasks, modeling distributions over functions rather than fixed parameters. They provide a probabilistic framework that outputs predictions with associated uncertainty estimates, making them particularly useful for scenarios where quantifying confidence is critical. GPs are defined by a mean function and a covariance (kernel) function, which captures the similarity between data points.

Also known as: GPs, Gaussian Process Regression, Kriging, GP, Gaussian Process Models

Why learn Gaussian Processes?

Developers should learn Gaussian Processes when working on problems requiring uncertainty quantification, such as Bayesian optimization for hyperparameter tuning, robotics, or financial modeling. They are ideal for small to medium-sized datasets where interpretability and probabilistic predictions are valued, and are commonly used in geostatistics (kriging) and experimental design. GPs excel in scenarios with noisy data or when prior knowledge about function smoothness needs to be incorporated via kernel choices.

Learning Resources

• book: Gaussian Processes for Machine Learning
• docs: Scikit-learn Gaussian Process Documentation
• tutorial: Gaussian Processes - A Visual Introduction
• course: Coursera: Gaussian Processes