Abstract
End-user interactive machine learning is a promising tool for enhancing human capabilities with large data. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. My dissertation work aims to advance our understanding of this question by investigating new techniques that move beyond naive or ad-hoc approaches and balance the needs of both end-users and machine learning algorithms. Although these explorations are grounded in specific applications, we endeavored to design strategies independent of application or domain specific features. As a result, our findings can inform future end-user interaction with machine learning systems.
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