Lowering the barrier to applying machine learning

Kayur Patel
2010 Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems - CHI EA '10  
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 machine
more » ... rning algorithms to train a model. I found that the key barrier to adoption is not a poor understanding of the machine learning algorithms themselves, but rather a poor understanding of the process for applying those algorithms and insufficient tool support for that process. I have created new programming and analysis tools that support programmers by helping them (1) implement machine learning systems and analyze results, (2) debug data, and (3) design and track experiments.
doi:10.1145/1753846.1753882 dblp:conf/chi/Patel10 fatcat:ctphoo6owzfnpf53ihq7kjix3e