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Probabilistic machine learning /

By: Material type: TextTextSeries: Adaptive computation and machine learning seriesPublisher: Cambridge, England : The MIT Press, [2022 - 2023]Copyright date: ©2022 - 2023Description: 2 volumes (various paging) : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780262046824
  • 9780262048439
Subject(s): DDC classification:
  • 006.31 MUP 23
Contents:
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.
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Holdings
Item type Current library Collection Call number Vol info Status Date due Barcode Item holds
Book Non-borrowing Book Non-borrowing Library D Information Technology 006.31 MUP (Browse shelf(Opens below)) Advanced Topics Not For Loan 1004551
Book Book Library D Information Technology 006.31 MUP (Browse shelf(Opens below)) Advanced Topics Available 1004552
Book Non-borrowing Book Non-borrowing Library D Information Technology 006.31 MUP (Browse shelf(Opens below)) An Introduction Not For Loan 1004549
Book Book Library D Information Technology 006.31 MUP (Browse shelf(Opens below)) An Introduction Available 1004550
Total holds: 0

Includes bibliographical references and index.

An Introduction: I Introduction -- II Linear Models -- III Deep Neural Networks -- IV Nonparametric Models -- V Beyond Supervised Learning.

Advanced Topics: I Introduction -- II Inference -- III Prediction -- IV Generation -- V Discovery -- VI Action.

"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.

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