Introduction to Algorithms for Data Mining and Machine Learning - Softcover

Yang, Xin-She

Softcover

ISBN 10: 0128172169 ISBN 13: 9780128172162

Publisher: Academic Press, 2019

Synopsis

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.

• Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics
• Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study
• Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

About this title

About the Author

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).

From the Back Cover

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.

Edition details

• Publisher: Academic Press
• Publication date: 2019
• Language: English
• ISBN 10: 0128172169
• ISBN 13: 9780128172162
• Binding: Paperback
• Edition number: 1
• Number of pages: 188