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Introduction to Algorithms for Data Mining and Machine Learning

  • 1st Edition - June 17, 2019
  • Latest edition
  • Author: Xin-She Yang
  • Language: English

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,… Read more

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Description

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.

Key features

  • 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

Readership

Undergraduates and graduates in computer science, management science, economics, and engineering will use the book in courses on data mining, machine learning, and optimization

Table of contents

1. Introduction2. Mathematical Foundations3. Data Fitting and Method of Least Squares4. Logistic Regression and PCA5. Data Mining6. Artificial Neural Networks7. Support Vector Machine8. Deep Learning

Product details

  • Edition: 1
  • Latest edition
  • Published: June 17, 2019
  • Language: English

About the author

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Xin-She Yang

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).
Affiliations and expertise
School of Science and Technology, Middlesex University, UK

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