  The notion of uncertainty is of major importance in machine learning and
constitutes a key element of machine learning methodology. In line with the
statistical tradition, uncertainty has long been perceived as almost synonymous
with standard probability and probabilistic predictions. Yet, due to the
steadily increasing relevance of machine learning for practical applications
and related issues such as safety requirements, new problems and challenges
have recently been identified by machine learning scholars, and these problems
may call for new methodological developments. In particular, this includes the
importance of distinguishing between (at least) two different types of
uncertainty, often referred to as aleatoric and epistemic. In this paper, we
provide an introduction to the topic of uncertainty in machine learning as well
as an overview of attempts so far at handling uncertainty in general and
formalizing this distinction in particular.
