Logo

Foundations of Machine Learning

Large book cover: Foundations of Machine Learning

Foundations of Machine Learning
by

Publisher: The MIT Press
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.

Home page url

Download or read it online for free here:
Read online
(online reading)

Similar books

Book cover: A Brief Introduction to Machine Learning for EngineersA Brief Introduction to Machine Learning for Engineers
by - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(6689 views)
Book cover: Statistical Learning and Sequential PredictionStatistical Learning and Sequential Prediction
by - University of Pennsylvania
This text focuses on theoretical aspects of Statistical Learning and Sequential Prediction. The minimax approach, which we emphasize throughout the course, offers a systematic way of comparing learning problems. We will discuss learning algorithms...
(6613 views)
Book cover: An Introduction to Statistical LearningAn Introduction to Statistical Learning
by - Springer
This book provides an introduction to statistical learning methods. It contains a number of R labs with detailed explanations on how to implement the various methods in real life settings and it is a valuable resource for a practicing data scientist.
(9856 views)
Book cover: The Hundred-Page Machine Learning BookThe Hundred-Page Machine Learning Book
by
This is the first successful attempt to write an easy to read book on machine learning that isn't afraid of using math. It's also the first attempt to squeeze a wide range of machine learning topics in a systematic way and without loss in quality.
(8755 views)