Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning

by Xin-She Yang
ISBN-10:
0128172169
ISBN-13:
9780128172162
Pub. Date:
06/19/2019
Publisher:
Elsevier Science
ISBN-10:
0128172169
ISBN-13:
9780128172162
Pub. Date:
06/19/2019
Publisher:
Elsevier Science
Introduction to Algorithms for Data Mining and Machine Learning

Introduction to Algorithms for Data Mining and Machine Learning

by Xin-She Yang
$74.95
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Overview

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.


Product Details

ISBN-13: 9780128172162
Publisher: Elsevier Science
Publication date: 06/19/2019
Pages: 188
Product dimensions: 5.90(w) x 8.90(h) x 0.50(d)

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).

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

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Engineered for introductory courses on data mining, machine learning and optimization

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