• Title: Machine Learning: A Probabilistic Perspective
• Author(s) Kevin Patrick Murphy
• Publisher: The MIT Press (September 7, 2012); eBook (Free PDF)
• Hardcover: 1104 Pages
• eBook: PDF (1098 pages)
• Language: English
• ASIN: B08FZLD4J4
• ISBN-10: 0262018020
• ISBN-13: 978-0262018029
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Book Description

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data.

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.

• Kevin Patrick Murphy is a Research Scientist at Google.

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• Machine Learning: A Probabilistic Perspective (Kevin Patrick Murphy)
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• Book Series

• Probabilistic Machine Learning: An Introduction (Kevin Murphy)

  This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

• Probabilistic Machine Learning: Advanced Topics (Kevin Murphy)

  This book expands the scope of Machine Learning to encompass more challenging problems, discusses methods for discovering 'insights' about data, and how to use probabilistic models for causal inference and decision making under uncertainty.

• An Introduction to Statistical Learning (Gareth James, et al)

  It provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

• Probabilistic Machine Learning for Civil Engineers

  This comprehensive textbook presents basic machine learning methods for civil engineers who do not have a specialized background in statistics or in computer science. It includes several case studies that students and professionals will appreciate.

• Bayesian Methods for Hackers: Probabilistic Programming

  This book illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, Matplotlib, through practical examples and computation - no advanced mathematics required.