This introduction aims to provide readers an intuitive understanding of  Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because  their representation flexibility and inherently uncertainty measures over predictions. The paper starts with explaining mathematical basics that Gaussian processes built on including multivariate normal distribution, kernels, non-parametric models, joint and conditional probability . The Gaussian processes regression is then described in an accessible way by balancing showing unnecessary mathematical derivation steps and missing key conclusive results. An illustrative implementation of a standard Gaussian processes regression algorithmis provided . Beyond the standard Gaussian processes regression, existing software packages to implement state-of-the-art Gaussian processes algorithms are reviewed. Lastly, more advanced Gaussian processes regression models are specified. The paper is written in an accessible way , thus undergraduate science and engineering background will find no difficulties in following the content .
This tutorial aims to provide an intuitive understanding of the Gaussian processes regression. Gaussian processes regression (GPR) models have been widely used in machine learning applications because of their representation flexibility and inherently uncertainty measures over predictions. The basic concepts that a Gaussian process is built on, including multivariate normal distribution, kernels, non-parametric models, joint and conditional probability were explained first. Next, the GPR was described concisely together with an implementation of a standard GPR algorithm . Beyond the standard GPR, packages to implement state-of-the-art Gaussian processes algorithms were reviewed. This tutorial was written in an accessible way to make sure readers without a machine learning background can obtain a good understanding of the GPR basics .
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