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Meta Alpha, LLC - Submitted to Opalesque as Guest Article - Confidential and Proprietary 1. Is it really machine learning, i.e. does it really learn? Many systems depend on traditional statistical techniques such as GARCH and other techniques but are not really machine learning. In other words, is there really learning, defined as automatic improvement with experience or data inputs. As Tom Mitchell, chair of the machine learning department at Carnegie Mellon University and co-founder of Meta Alpha says: "The field of Machine Learning seeks to answer the
question: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?" Tom further writes: Machine Learning is a natural outgrowth of the intersection of Computer Science and Statistics. We might say the defining question of Computer Science is "How can we build machines that solve problems, and which problems are inherently tractable/intractable?" The question that largely defines Statistics is "What can be inferred from data plus a set of modeling assumptions, with what reliability?" The defining question for Machine Learning builds on both, but it is a distinct question. Whereas Computer Science has focused primarily on how to manually program computers, Machine Learning focuses on the question of how to get computers to program themselves (from experience plus some initial structure).
Whereas Statistics has foc...................... To view our full article Click here
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