Machine learning
Subtitle: a probabilistic perspective
By: Kevin P. Murphy
Series: Adaptive computation and machine learning series
Publisher: MIT Press
Published: 2012
Place of publication: Cambridge, MA
Language: eng
Number of pages: 1104
ISBN-13: 9780262018029
Subjects: Probabilities; Machine learning
Description: "This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover.
Notes: Includes bibliographical references and index.