Logo

Understanding Machine Learning: From Theory to Algorithms

Large book cover: Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning: From Theory to Algorithms
by

Publisher: Cambridge University Press
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.

Home page url

Download or read it online for free here:
Download link
(2.5MB, PDF)

Similar books

Book cover: Reinforcement Learning: An IntroductionReinforcement Learning: An Introduction
by - 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.
(26898 views)
Book cover: Foundations of Machine LearningFoundations of Machine Learning
by - The MIT Press
This is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools.
(6216 views)
Book cover: The Elements of Statistical Learning: Data Mining, Inference, and PredictionThe Elements of Statistical Learning: Data Mining, Inference, and Prediction
by - 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.
(40092 views)
Book cover: Machine LearningMachine Learning
by - InTech
Neural machine learning approaches, Hamiltonian neural networks, similarity discriminant analysis, machine learning methods for spoken dialogue simulation and optimization, linear subspace learning for facial expression analysis, and more.
(16231 views)