  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.
