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Concise Introduction to Machine Learning 2nd edition [Pehme köide]

(University of Cambridge, UK)
  • Formaat: Paperback / softback, 324 pages, kõrgus x laius: 234x156 mm, kaal: 600 g, 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
  • Sari: Chapman & Hall/CRC Machine Learning & Pattern Recognition
  • Ilmumisaeg: 15-May-2025
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1032878142
  • ISBN-13: 9781032878140
Teised raamatud teemal:
Concise Introduction to Machine Learning 2nd editionSuurem pilt
  • Formaat: Paperback / softback, 324 pages, kõrgus x laius: 234x156 mm, kaal: 600 g, 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
  • Sari: Chapman & Hall/CRC Machine Learning & Pattern Recognition
  • Ilmumisaeg: 15-May-2025
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1032878142
  • ISBN-13: 9781032878140
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032878140.html
Märksõnad:

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.



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.

Arvustused

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 Anitas 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

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

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.