Introduction to Algorithms for Data Mining and Machine Learning - Tapa blanda

Yang, Xin-She

 
9780128172162: Introduction to Algorithms for Data Mining and Machine Learning
Tapa blanda
ISBN 10: 0128172169 ISBN 13: 9780128172162
Editorial: Academic Press, 2019

Sinopsis

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

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Acerca del autor

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).

De la contraportada

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning as well as optimization. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modelling skills so they can process and interpret data for classification, clustering, curve-fitting, and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.

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Editorial
Academic Press
Año de publicación
2019
Idioma
Inglés
ISBN 10 
0128172169
ISBN 13 
9780128172162
Encuadernación
Tapa blanda
Número de páginas
188
Contacto del fabricante
no disponible
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