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Lowering the Barrier to Applying Machine Learning Data is driving the future of computation: analysis, visualization, and learning algorithms power systems that help us diagnose cancer, live sustainably, and understand the universe. Yet, the data explosion has outstripped our tools to process it, leaving a gap between powerful new algorithms and what real programmers can apply in practice. I examine how data affects the way we program. Specifically, this dissertation focuses on using machinedoi:10.1145/1753846.1753882 dblp:conf/chi/Patel10 fatcat:ctphoo6owzfnpf53ihq7kjix3e