Foundations of Machine Learning
by M. Mohri, A. Rostamizadeh, A. Talwalkar
Publisher: The MIT Press 2018
ISBN-13: 9780262039406
Number of pages: 504
Description:
This book 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 needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
Download or read it online for free here:
Read online
(online reading)
Similar books
Gaussian Processes for Machine Learningby Carl E. Rasmussen, Christopher K. I. Williams - The MIT Press
Gaussian processes provide a principled, practical, probabilistic approach to learning in kernel machines. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics.
(26204 views)
A Survey of Statistical Network Modelsby A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi - arXiv
We begin with the historical development of statistical network modeling and then we introduce some examples in the network literature. Our subsequent discussion focuses on prominent static and dynamic network models and their interconnections.
(7250 views)
Machine Learning: A Probabilistic Perspectiveby Kevin Patrick Murphy - The MIT Press
A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.
(2373 views)
Machine Learning for Data Streamsby Albert Bifet, et al. - The MIT Press
This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.
(5192 views)