  Uncertainty in machine learning is not generally taught as general knowledge
in Machine Learning course curricula. In this paper we propose a short
curriculum for a course about uncertainty in machine learning, and complement
the course with a selection of use cases, aimed to trigger discussion and let
students play with the concepts of uncertainty in a programming setting. Our
use cases cover the concept of output uncertainty, Bayesian neural networks and
weight distributions, sources of uncertainty, and out of distribution
detection. We expect that this curriculum and set of use cases motivates the
community to adopt these important concepts into courses for safety in AI.
