Using Domain-Knowledge to Improve Machine Learning

Machine Learning methods have achieved some impressive results over the past decade. However, this success was in large part a result of utilizing large amounts of data and growing computational resources efficiently. To extend this recent success to domains where large quantities of high-quality data are not readily available, the field of informed machine learning has emerged, which aims at integrating preexisting knowledge into machine learning models. The aim of this paper is to provide an overview of the major new developments in this field and to discuss important open problems.