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Machine Learning: The Art and Science of Algorithms that Make Sense of Data Paperback – 20 September 2012
by
Peter Flach
(Author)
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As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
- ISBN-101107422221
- ISBN-13978-1107422223
- Edition1st
- PublisherCambridge University Press
- Publication date20 September 2012
- LanguageEnglish
- Dimensions18.9 x 2.49 x 24.61 cm
- Print length416 pages
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Product description
Review
"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms."
Fernando Berzal, Computing Reviews
Fernando Berzal, Computing Reviews
Book Description
Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
About the Author
Peter Flach has more than twenty years of experience in machine learning teaching and research. He is Editor-in-Chief of Machine Learning and Program Co-Chair of the 2009 ACM Conference on Knowledge Discovery and Data Mining and the 2012 European Conference on Machine Learning and Data Mining. His research spans all aspects of machine learning, from knowledge representation and the use of logic to learn from highly structured data to the analysis and evaluation of machine learning models and methods to large-scale data mining. He is particularly known for his innovative use of Receiver Operating Characteristic (ROC) analysis for understanding and improving machine learning methods. These innovations have proved their effectiveness in a number of invited talks and tutorials and now form the backbone of this book.
Product details
- Publisher : Cambridge University Press; 1st edition (20 September 2012)
- Language : English
- Paperback : 416 pages
- ISBN-10 : 1107422221
- ISBN-13 : 978-1107422223
- Dimensions : 18.9 x 2.49 x 24.61 cm
- Best Sellers Rank: 707,674 in Books (See Top 100 in Books)
- 227 in Computer Vision & Pattern Recognition
- 637 in Programming Algorithms
- 3,148 in Computer Science Textbooks
- Customer Reviews:
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Customer reviews
4.2 out of 5 stars
4.2 out of 5
77 global ratings
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Top reviews from other countries

Martin Castillo Cabrera
5.0 out of 5 stars
Muy actualizado
Reviewed in Mexico on 13 April 2019Verified Purchase
Buen libro

Angelo Rossi
5.0 out of 5 stars
Testo di intelligenza artificiale molto pratico.
Reviewed in Italy on 14 July 2019Verified Purchase
Introduce in maniera chiara e con esempi concreti agli algoritmi dell'intelligenza artificiale e specificatamente del machine learning. Ottimo testo di base.

John
5.0 out of 5 stars
Approachable, dense, and beautiful book on a wonderful subject
Reviewed in the United States on 29 December 2014Verified Purchase
What an amazing book, I got it about a month ago for a self-study routine and every page of this book has been a joy. I am an undergraduate CS major with a decent amount of math experience, and for me this book is a tough but rewarding read. I constantly find myself reading the same section 2 or 3 times in a row, restling with the concepts until I can grasp some intuition of the topics bring discussed. The author is very thorough in their writing, making sure to fill in the details so you dont get left behind in the mathematical notation. The book is filled with beautiful graphs and other figures to further help the reader along in their understanding of machine learning.
As a heads up, this book is heavy on the theory and light on the application, so keep that in mind when considering this book for purchase. It isn't going to give you a simple recipe to plug into R. It did however, lay out the intricacies of machine learning in a very abstract and methodical fashion, allowing the reader to gain a much deeper insight into the mechanics of the popular ML techniques than a more practical book would.
As a heads up, this book is heavy on the theory and light on the application, so keep that in mind when considering this book for purchase. It isn't going to give you a simple recipe to plug into R. It did however, lay out the intricacies of machine learning in a very abstract and methodical fashion, allowing the reader to gain a much deeper insight into the mechanics of the popular ML techniques than a more practical book would.
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