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Patterns, Predictions, and Actions

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Hardback
320 Pages
24-January-2023
RRP: $89.99
$86.00

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts
Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions.

  • Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions
  • Pays special attention to societal impacts and fairness in decision making
  • Traces the development of machine learning from its origins to today
  • Features a novel chapter on machine learning benchmarks and datasets
  • Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra
  • An essential textbook for students and a guide for researchers

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    Product Details
    ISBN-13
    9780691233734
    Author
    Moritz Hardt
    Publisher
    Princeton University Press
    Published
    24-January-2023
    Format
    Hardback
    Pages
    320

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    RRP: $89.99
    $86.00

    Patterns, Predictions, and Actions

    RRP: $89.99
    $86.00

    Description

    An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts
    Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions.

    • Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions
    • Pays special attention to societal impacts and fairness in decision making
    • Traces the development of machine learning from its origins to today
    • Features a novel chapter on machine learning benchmarks and datasets
    • Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra
    • An essential textbook for students and a guide for researchers

    You Can Find This Title In

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