Concise Introduction to Machine Learning

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.

List of contents

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

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.

Summary

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® and can be run in Binder in a web browser. Each chapter concludes with exercises to explore the content.

Product details

• Authors: A.C. Faul, A.c. (University of Cambridge Faul
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 22.04.2025
• EAN: 9781032878140
• ISBN: 978-1-0-3287814-0
• No. of pages: 324
• Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Data Mining, Robotics, Artificial Intelligence, COMPUTERS / General, COMPUTERS / Computer Science, COMPUTERS / Programming / Algorithms, COMPUTERS / Artificial Intelligence / General, COMPUTERS / Data Science / Machine Learning