The paper presents interdisciplinary research in the intersection of AI (machine learning) and Art (music). We describe an implemented system that learns expressive interpretation of music pieces from performances by human musicians. The problem, shown to be very difficult in the introduction, is solved by combining insights from music theory with a new machine learning algorithm. Theoretically founded knowledge about music perception is used to transform the original learning problem to a more abstract level where relevant regularities become apparent. Experiments with performances of Chopin waltzes are presented; the results indicate musical understanding and the ability to learn a complex task from very little training data. As the system’s domain knowledge is based on two established theories of tonal music, the results also have interesting implications for music theory.