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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Probabilistic Machine Learning: An Introduction (Adaptive Computation and
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Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) Hardback - 2022

by Murphy, Kevin P

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The MIT Press, 3/1/2022 12:00:01 AM. hardcover. Good. 1.2598 in x 9.3307 in x 8.2677 in. Bruise/tear to cover.
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Details

  • Title Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
  • Author Murphy, Kevin P
  • Binding Hardback
  • Condition Used - Good
  • Pages 864
  • Volumes 1
  • Language ENG
  • Publisher The MIT Press
  • Publication date 3/1/2022 12:00:01 AM
  • Features Bibliography, Index
  • Bookseller's Inventory # mon0000038761
  • ISBN 9780262046824 / 0262046822
  • Weight 3.4 lbs (1.54 kg)
  • Dimensions 9.13 x 8.03 x 1.5 in (23.19 x 20.40 x 3.81 cm)
  • Size 1.2598 in x 9.3307 in x 8.2677 i
  • Category Computers - General Information
  • Library of Congress subjects Probabilities, Machine learning
  • Library of Congress Catalogue Number 2021027430
  • Dewey Decimal Code 006.31
  • Quantity available 1

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From the publisher

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.

This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation.

Probabilistic Machine Learning grew out of the author's 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

About the author

Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding.

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