“Introduction to Gaussian processes”

Abstract: Gaussian processes are a class of prior distributions over functions widely used in machine learning. The merit of Gaussian processes is that we can handily encode the prior knowledge into a model at hand, and simultaneously quantify uncertainty. In this lecture, we introduce the fundamentals of Gaussian processes by explaining the definition and motivations of using them. Then, we show how to draw samples from Gaussian processes, and solve the regression problems with real examples. The lecture is partially based on and simplified from https://github.com/spdes/gp.