Machine learning holds the key for massive waves that are already starting to fundamentally change business, from targeted advertising, to personalization, to real-time data-driven business processes. But is ML really possible on big data with state-of-the-art methods (which yield the highest predictive accuracies), or just simple ones (such as linear models)? Can ML really be done in real time today? Is MapReduce really the best technical solution to large-scale ML? Does it really make sense to send data to the cloud and do ML there? In this talk I will review the current state of machine learning technology both at the research level and the industry-readiness level, and current best solution options.
This session is sponsored by Skytree, Inc
Dr. Gray obtained degrees in Applied Mathematics and Computer Science from Berkeley and a PhD in Computer Science from Carnegie Mellon, and is an Associate Professor at Georgia Tech. His lab works to scale up all of the major practical methods of machine learning (ML) to massive datasets. He began working on this problem at NASA in 1993 (long before the current fashionable talk of “big data”). His large-scale algorithms helped enable the Top Scientific Breakthrough of 2003, and have won a number of research awards. He is a member of the National Academy of Sciences Committee on the Analysis of Massive Data and frequently gives invited tutorial lectures on massive-scale ML at top research conferences and agencies.
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