Gaussian processes (GPs) are a powerful, non-parametric Bayesian method that can be used in supervised learning and classification problems. The main advantages of this method are the ability of GPs to provide uncertainty estimates and to learn functions of the underlying phenomena from training data. The aim of this meetup is to introduce GPs for regression and classification with Stan. We will discuss the basics of GPs as a nonparametric Bayesian method. We will also discuss how to build GP models in computer code using a new exciting programming paradigm called Probabilistic Programming (PP). Particularly we shall use PyMC3, a PP language, to build GP models for regression and classification.