  This chapter presents an overview of Interactive Machine Learning (IML)
techniques applied to the analysis and design of musical gestures. We go
through the main challenges and needs related to capturing, analysing, and
applying IML techniques to human bodily gestures with the purpose of performing
with sound synthesis systems. We discuss how different algorithms may be used
to accomplish different tasks, including interacting with complex synthesis
techniques and exploring interaction possibilities by means of Reinforcement
Learning (RL) in an interaction paradigm we developed called Assisted
Interactive Machine Learning (AIML). We conclude the chapter with a description
of how some of these techniques were employed by the authors for the
development of four musical pieces, thus outlining the implications that IML
have for musical practice.
