Kevin P. Murphy

Kevin P. Murphy is a Senior Staff Research Scientist at Google Research.

  • Machine Learning, Second Edition

    A Probabilistic Perspective

    Kevin P. Murphy

    The second and expanded edition of a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

    This textbook offers a comprehensive and self-contained introduction to the field of machine learning, including deep learning, viewed through the lens of probabilistic modeling and Bayesian decision theory. This second edition has been substantially expanded and revised, incorporating many recent developments in the field. It has new chapters on linear algebra, optimization, implicit generative models, reinforcement learning, and causality; and other chapters on such topics as variational inference and graphical models have been significantly updated. The software for the book (hosted on github) is now implemented in Python rather than MATLAB, and uses state-of-the-art libraries including as scikit-learn, Tensorflow 2, and JAX. 

    The book combines breadth and depth. Part 1, on mathematical foundations, covers such topics as probability, statistics, and linear algebra; Part 2, on algorithmic methods, covers such topics as optimization, variational inference, and Monte Carlo sampling; and Part 3, on models, covers such topics as linear models, neural networks, and graphical models. All topics are copiously illustrated with color images and worked examples drawn from application domains including biology, natural language processing, computer vision, and robotics. Exercises are available online. The book is suitable for graduate students and upper-level undergraduates in a variety of quantitative fields, or indeed anyone with an introductory-level college math background.

    • Hardcover $120.00 £98.00
  • Machine Learning

    Machine Learning

    A Probabilistic Perspective

    Kevin P. Murphy

    A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

    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. 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. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    • Hardcover $110.00 £90.00