Understanding Machine Learning: From Theory to Algorithms
by Shai Shalev-Shwartz, Shai Ben-David
Publisher: Cambridge University Press 2014
ISBN/ASIN: 1107057132
ISBN-13: 9781107057135
Number of pages: 449
Description:
The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
Download or read it online for free here:
Download link
(2.5MB, PDF)
Similar books
Reinforcement Learning: An Introductionby Richard S. Sutton, Andrew G. Barto - The MIT Press
The book provides a clear and simple account of the key ideas and algorithms of reinforcement learning. It covers the history and the most recent developments and applications. The only necessary mathematical background are concepts of probability.
(27410 views)
Machine Learning, Neural and Statistical Classificationby D. Michie, D. J. Spiegelhalter - Ellis Horwood
The book provides a review of different approaches to classification, compares their performance on challenging data-sets, and draws conclusions on their applicability to realistic industrial problems. A wide variety of approaches has been taken.
(28410 views)
The Elements of Statistical Learning: Data Mining, Inference, and Predictionby T. Hastie, R. Tibshirani, J. Friedman - Springer
This book brings together many of the important new ideas in learning, and explains them in a statistical framework. The authors emphasize the methods and their conceptual underpinnings rather than their theoretical properties.
(40677 views)
Boosting: Foundations and Algorithmsby Robert E. Schapire, Yoav Freund - The MIT Press
Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate 'rules of thumb'. A remarkably rich theory has evolved around boosting, with connections to a range of topics.
(6747 views)