Abstract

This thesis examines machine learning through the lens of human-computer interaction in order to address fundamental questions surrounding the application of machine learning to real-life problems, including: Can we make machine learning algorithms more usable and useful? Can we better understand the real-world consequences of algorithm choices and user interface designs for end-user machine learning? How can human interaction play a role in enabling users to efficiently create useful machine learning systems, in enabling successful application of algorithms by machine learning novices, and in ultimately making it possible in practice to apply machine learning to new problems? The scope of the research presented here is the application of supervised learning algorithms to contemporary computer music composition and performance. Computer music is a domain rich with computational problems requiring the modeling of complex phenomena, the construction of real-time interactive systems, and the support of human creativity. Though varied, many of these problems may be addressed

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