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Probabilistic Machine Learning : an introduction

By: Material type: TextTextSeries: Adaptive computation and machine learning seriesPublication details: Cambridge: MIT press, 2022Description: xxix, 826 pages : illustrations (some color)ISBN:
  • 9780262046824
Subject(s): DDC classification:
  • 006.3 MUR
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"--
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Holdings
Item type Current library Home library Call number Status Date due Barcode
Book Book Dept. of Statistics Dept. of Statistics 006.3 MUR (Browse shelf(Opens below)) Available STA10798

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"--

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