A Concise Introduction to Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition). This item is unavailable.
Language: English
Published by Chapman and Hall/CRC, 2025
- Softcover
- New

Seller: Ria Christie Collections, Uxbridge, United KingdomRia Christie Collections
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Condition: New
£ 74.98
Item description from seller
In English.
Seller Inventory # ria9781032878140_new
- Title
- A Concise Introduction to Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
- Author
- Faul, A.C.
- Publisher
- Chapman and Hall/CRC
- Publication year
- 2025
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1032878142
- ISBN 13
- 9781032878140
- Edition
- 2nd Edition
- Item weight
- 706 grams
A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles and illustrates every concept using examples in both Python and MATLAB®, which are available on GitHub and can be run from there in Binder in a web browser. Each chapter concludes with exercises to explore the content.
The emphasis of the book is on the question of Why―only if “why” an algorithm is successful is understood, can it be properly applied and the results trusted. Standard techniques are treated rigorously, including an introduction to the necessary probability theory. This book addresses the commonalities of methods, aims to give a thorough and in-depth treatment and develop intuition for the inner workings of algorithms, while remaining concise.
This useful reference should be essential on the bookshelf of anyone employing machine learning techniques, since it is born out of strong experience in university teaching and research on algorithms, while remaining approachable and readable.
"Synopsis" may belong to another edition of this title.
About the Author
A.C. Faul is a passionate educator believing that only with deep understanding of the underlying connecting principles of algorithms can progress be made. She obtained an MASt and PhD in Mathematics at the University of Cambridge. She has worked on a variety of algorithms both in industry and academic settings.
"About the title" may belong to another edition of this title.