Understanding Machine Learning

From Theory to Algorithms



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Understanding Machine Learning
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AuthorsShai Shalev-Shwartz, Shai Ben-David
PublisherSelf-publishing
Published2014
Pages449
LanguageEnglish
FormatPaper book / ebook (PDF)
ISBN-101107057132
ISBN-139781107057135
EBook Hardcover Paperback

The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can "learn" from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we'll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)?





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