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

4.5 out of 5 stars 59 ratings

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Editorial Reviews

Review

“The deep learning revolution has transformed the field of machine learning over the last decade. It was inspired by attempts to mimic the way the brain learns but it is grounded in basic principles of statistics, information theory, decision theory, and optimization. This book does an excellent job of explaining these principles and describes many of the ‘classical’ machine learning methods that make use of them. It also shows how the same principles can be applied in deep learning systems that contain many layers of features. This provides a coherent framework in which one can understand the relationships and tradeoffs between many different ML approaches, both old and new.”
Geoffrey Hinton, Emeritus Professor of Computer Science, University of Toronto; Engineering Fellow, Google

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. 
 

Product details

  • Publisher ‏ : ‎ The MIT Press (March 1, 2022)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 864 pages
  • ISBN-10 ‏ : ‎ 0262046822
  • ISBN-13 ‏ : ‎ 978-0262046824
  • Item Weight ‏ : ‎ 3.4 pounds
  • Dimensions ‏ : ‎ 8.25 x 1.44 x 9.29 inches
  • Customer Reviews:
    4.5 out of 5 stars 59 ratings

About the author

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Kevin Patrick Murphy was born in Ireland, grew up in England (BA from Cambridge),

and went to graduate school in the USA (MEng from U. Penn, PhD from UC Berkeley,

Postdoc at MIT). In 2004, he became a professor of computer science and statistics

at the University of British Columbia in Vancouver, Canada. In 2011, he went to

Google in Mountain View, California for his sabbatical. In 2012, he

converted to a full-time research scientist position at Google. Kevin has

published over 50 papers in refereed conferences and journals related

to machine learning and graphical models. He has recently published

an 1100-page textbook called "Machine Learning: a Probabilistic

Perspective" (MIT Press, 2012).

Customer reviews

4.5 out of 5 stars
4.5 out of 5
59 global ratings

Top reviews from the United States

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5.0 out of 5 stars Perfect Solution for our Needs
By Sharlotte Hardin Hughes on June 15, 2022
We moved a refrigerator in our garage and needed a longer cord to reach the electrical outlet. This one was the appropriate length. We were impressed with the sturdiness of the product. We are very pleased with this cord.
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Top reviews from other countries

Muhammad Bello
5.0 out of 5 stars Really Love This
Reviewed in the United Kingdom on April 6, 2022
One person found this helpful
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Devendra Kumar Sahu
5.0 out of 5 stars Best machine learning book. Period.
Reviewed in India on April 23, 2022
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5.0 out of 5 stars Best machine learning book. Period.
Reviewed in India on April 23, 2022
I am an applied scientist at a major tech company and first version of this book helped me immensely during my MS program. I have read half of this book last week and it is as good as i was expecting it to be.

I don't have enough words to praise for both of these volumes and forever be grateful to the author for writing this masterpiece.

Note: the book is in colour just like any other MIT press books.
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2 people found this helpful
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JYauri
3.0 out of 5 stars Printed on sheets almost transparent
Reviewed in Spain on July 23, 2022
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3.0 out of 5 stars Printed on sheets almost transparent
Reviewed in Spain on July 23, 2022
The content is well organized and of high academic quality. Better than online tutorials, I recommend for people interested in master machine learning. I liked the understandable mathematical formulation.

On the other hand, I am disappointed with the sheets used in this book. The sheets are thin and almost transparent that distract the reader.
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Lilly
3.0 out of 5 stars Great book, but I received a damaged copy
Reviewed in Canada on March 14, 2022
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3.0 out of 5 stars Great book, but I received a damaged copy
Reviewed in Canada on March 14, 2022
This great book definitely worth a 5-star rating. I minus two stars, since it is badly protected in shipping and I received a damaged copy.
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