  Machine learning is the capacity of a computational system to learn
structures from datasets in order to make predictions on newly seen data. Such
an approach offers a significant advantage in music scenarios in which
musicians can teach the system to learn an idiosyncratic style, or can break
the rules to explore the system's capacity in unexpected ways. In this chapter
we draw on music, machine learning, and human-computer interaction to elucidate
an understanding of machine learning algorithms as creative tools for music and
the sonic arts. We motivate a new understanding of learning algorithms as
human-computer interfaces. We show that, like other interfaces, learning
algorithms can be characterised by the ways their affordances intersect with
goals of human users. We also argue that the nature of interaction between
users and algorithms impacts the usability and usefulness of those algorithms
in profound ways. This human-centred view of machine learning motivates our
concluding discussion of what it means to employ machine learning as a creative
tool.
