A Concise Introduction to Machine Learning
2nd Edition
Published on 14. May 2025
352 pages
978-1-040-32689-3 (ISBN)
Description
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 (R), 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.
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.
Reviews / Votes
"Artificial intelligence technologies will be transformational for our society, but not in the manner that the confident proclaimers of utopia and dystopia suggest. The changes we expect are sociotechnical; they require careful understanding from both the societal and technical perspectives. This is where Anita Faul's A Concise Introduction to Machine Learning comes in. With comprehensive coverage of the mathematical principles of machine learning, Anita's book provides the foundation of principled choices that students need to make good decisions when separating the noise from the signal. This foundation is critical to ensuring that we develop the beneficial aspects of the technology while curtailing the damaging outcomes. This can only occur through comprehensive understanding of the capabilities and limitations of these techniques.Welcome to Anita's world: a concise introduction to machine learning that builds on solid foundations, practical real-world experience and a long experience of sharing these principles with students."
- Neil Lawrence, The DeepMind Professor of Machine Learning, University of Cambridge. Senior AI Fellow, The Alan Turing Institute. Visiting Professor of Machine Learning, University of Sheffield
More details
Series
Edition
2nd edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Professional and scholarly
Illustrations
15 Tables, black and white; 63 Line drawings, color; 42 Line drawings, black and white; 4 Halftones, color; 67 Illustrations, color; 42 Illustrations, black and white
File size
9,36 MB
ISBN-13
978-1-040-32689-3 (9781040326893)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
05/2025
2nd Edition
Chapman & Hall/CRC
€214.00
Shipment within 10-20 days
Book
05/2025
2nd Edition
Chapman & Hall/CRC
€101.60
Shipment within 10-20 days
A. C. Faul
A Concise Introduction to Machine Learning
Book
05/2025
CRC Press
Unfortunately, price unknown
Available (delivery time upon request)
Person
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.
Content
Chapter 1. Introduction
Chapter 2. Probability Theory
Chapter 3. Sampling
Chapter 4. Linear Classification
Chapter 5. Non-Linear Classification
Chapter 6. Dimensionality Reduction
Chapter 7. Regression
Chapter 8. Feature Learning
Appendix A. Matrix Formulae
Index
Chapter 2. Probability Theory
Chapter 3. Sampling
Chapter 4. Linear Classification
Chapter 5. Non-Linear Classification
Chapter 6. Dimensionality Reduction
Chapter 7. Regression
Chapter 8. Feature Learning
Appendix A. Matrix Formulae
Index
