Probabilistic machine learning : an introduction / Kevin P. Murphy.

By:

Murphy, Kevin P, 1970- [author.]

Material type: Text

TextSeries: Adaptive computation and machine learning seriesPublisher: Cambridge, Massachusetts : The MIT Press, [2022]Description: xxix, 826 pages : illustrations (some color) ; 24 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780262046824

Subject(s):

DDC classification:

006.3/1 23

LOC classification:

Q325.5 .M872 2022

Summary: "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"-- Provided by publisher.

Holdings ( 1 )
Title notes ( 2 )

Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Book	Library	006-2022 (Browse shelf(Opens below))	Available		AT-ISTA#002935

Total holds: 0

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Previous	006-2022 Deep learning with Python /	006-2022 Handbook of machine learning applications for genomics /	006-2022 Elements of digital geometry, mathematical morphology, and discrete optimization /	006-2022 Probabilistic machine learning : an introduction /	006-2023 Fast Forward: How to Harness the Power of AI for Societal Progress and a Sustainable Future	006-2023 What is ChatGPT doing ... and why does it work? /	006-2023 Google JAX Essentials : a quick practical learning of blazing-fast library for machine learning and deep learning projects	Next

Includes bibliographical references and index.

"This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"-- Provided by publisher.