This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR). GPR models have been widely used in machine learning applications due to their representation flexibility and inherent capability to quantify uncertainty over predictions. The tutorial starts with explaining the basic concepts that a Gaussian process is built on, including multivariate normal distribution, kernels, non-parametric models, and joint and conditional probability. It then provides a concise description of GPR and an implementation of a standard GPR algorithm. In addition, the tutorial reviews packages for implementing state-of-the-art Gaussian process algorithms. This tutorial is accessible to a broad audience, including those new to machine learning, ensuring a clear understanding of GPR fundamentals.

翻译：本教程旨在提供对高斯过程回归的直观介绍。由于其表示灵活性以及内在的预测不确定性量化能力，高斯过程回归模型已在机器学习应用中得到广泛应用。教程首先阐释构建高斯过程的基本概念，包括多元正态分布、核函数、非参数模型以及联合概率与条件概率。随后，对高斯过程回归进行简明描述，并给出标准高斯过程回归算法的实现。此外，教程还回顾了用于实现先进高斯过程算法的软件包。本教程面向包括机器学习初学者在内的广泛读者，确保对高斯过程回归的基本原理有清晰理解。