  This paper summarizes some challenges encountered and best practices
established in several years of teaching Machine Learning for the Physical
Sciences at the undergraduate and graduate level. I discuss motivations for
teaching ML to physicists, desirable properties of pedagogical materials, such
as accessibility, relevance, and likeness to real-world research problems, and
give examples of components of teaching units.
