Tipo: materialTypeLabelLibro - General
Ubicación Física: 006.3 / M681

Machine learning /

Autor: Mitchell, Tom M.
Pié de imprenta: Boston (Estados Unidos) : McGraw-Hill, 1997.
Descripción: 414 p.
ISBN: 9780070428072.
Tema(s):
Resumen: Machine Learning is the study of computer algorithms that improve automatically through experience. Successful applications range from data mining programs that discover general rules from large databases, to information filtering systems that learn users reading preferences, to autonomous vehicles that learn to drive on public highways. Machine Learning is an inherently interdisciplinary field, built on concepts from artificial intelligence, probability and statistics, information theory, philosophy, control theory, psychology, neurobiology, and other fields. This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field, No prior background in artificial intelligence or statistics is assumed. Several key algorithms, example date sets, and project-oriented homework assignments discussed in the book are accessible through the World Wide Web.

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Colección General 006.3 / M681 (Navegar estantería) Ej. 1 Disponible Reserva 49401
Libro - General Libro - General BIBLIOTECA SEDE LA CARO
Depósito
Colección General 006.3 / M681 (Navegar estantería) Ej. 2 Disponible 49562

Sistemas

Machine Learning is the study of computer algorithms that improve automatically through experience. Successful applications range from data mining programs that discover general rules from large databases, to information filtering systems that learn users reading preferences, to autonomous vehicles that learn to drive on public highways. Machine Learning is an inherently interdisciplinary field, built on concepts from artificial intelligence, probability and statistics, information theory, philosophy, control theory, psychology, neurobiology, and other fields. This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field, No prior background in artificial intelligence or statistics is assumed. Several key algorithms, example date sets, and project-oriented homework assignments discussed in the book are accessible through the World Wide Web.

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