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386 pages, Paperback
Published February 27, 2018
Since the dawn of the technological age, researchers have dreamed of teaching computers to reason and make intelligent decisions in the way that humans do, by drawing generalizations and distilling concepts from complex information sets without explicit instructions.
Machine learning refers to one aspect of this goal- specifically, to algorithms and processes that learn in the sense of being able to generalize past data and experiences in order to predict future outcomes.
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At the most general level, supervised machine learning methods adopt a Bayesian approach to knowledge discovery, using probabilities of previously observed events to infer the probabilities of new events. Unsupervised methods draw abstracts from unlabeled datasets and apply these to new data. Both families of methods can be applied to problems of classifications (assigning observations to categories) or regression (predicting numerical properties of an observation).