Machine Learning: The Art and Science of Algorithms that Make Sense of Data


€165,37
Auteur Peter Flach
Taal ENG- Engels
Bindwijze Paperback
ISBN/EAN 9781107096394
Releasedatum 2012-09-20
Doelgroep Tieners en jongvolwassenen, Volwassenen, Volwassenen en jong volwassenen
Title: Default Title
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Sonderpreis€165,37

Machine Learning: The Art and Science of Algorithms that Make Sense of Data van Peter Flach is een Engelstalig gedrukt boek. Deze titel is geschikt voor studenten in het hoger onderwijs en professionals.

As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

Machine Learning brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, the book explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.

Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

Inhoudelijk sluit dit boek aan bij onderwerpen als Machine learning, Algorithms and data structures, COMPUTERS / Data Science / Machine Learning.

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