Abstract

To help teaching and learning of machine learning courses, properties and application examples of Gaussian distributions in machine learning are presented, including classification, regression, and approximation. In each typical cases, emphasis is placed on the assumptions and the conclusions related to Gaussian distributions, so that students can make clear that it is the Gaussian distribution that make the somewhat abstract Bayes’ theorem more concrete and yields closed-form analytical predictive distributions.