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Inference and Learning from Data: Volume 1: Foundations

Inference and Learning from Data: Volume 1: Foundations

Inference and Learning from Data: Volume 1: Foundations
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Inference and Learning from Data: Volume 1: Foundations Hardback - 2022

by Sayed, Ali H

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Cambridge University Press, 22/12/2022 00:00:01. hardcover. Very Good. 4.3967 24.8811 18.0863.
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Details

  • Title Inference and Learning from Data: Volume 1: Foundations
  • Author Sayed, Ali H
  • Binding Hardback
  • Condition Used - Very good
  • Pages 1010
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 22/12/2022 00:00:01
  • Bookseller's Inventory # mon0000299289
  • ISBN 9781009218122 / 1009218123
  • Weight 3.95 lbs (1.79 kg)
  • Dimensions 9.37 x 6.61 x 1.1 in (23.80 x 16.79 x 2.79 cm)
  • Size 4.3967 24.8811 18.0863
  • Category Technology & Industrial Arts
  • Quantity available 2

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Reader reviews for Inference and Learning from Data: Volume 1: Foundations

From the publisher

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This first volume, Foundations, introduces core topics in inference and learning, such as matrix theory, linear algebra, random variables, convex optimization and stochastic optimization, and prepares students for studying their practical application in later volumes. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 600 end-of-chapter problems (including solutions for instructors), 100 figures, 180 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Inference and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
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