• Patterns, Predictions, and Actions
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Patterns, Predictions, and Actions

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An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impactsPatterns, Predictions, and Actions introduces graduate students to the ...
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  • Publication Date: 18 October 2022
  • ISBN: 9780691233734
  • Pages: 320
  • Imprint: Princeton University Press
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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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Price: $68.00
Pages: 320
Publisher: Princeton University Press
Imprint: Princeton University Press
Publication Date: 18 October 2022
ISBN: 9780691233734
Format: Hardcover
"A thorough, very clearly written overview on the subject of machine learning for those with the prerequisite mathematical tools of calculus, linear algebra and probability."---Jonathan Shock, Mathemafrica
Moritz Hardt is a director at the Max Planck Institute for Intelligent Systems. Benjamin Recht is professor of electrical engineering and computer sciences at the University of California, Berkeley.
  • List of Figures
  • List of Tables
  • Preface
  • Acknowledgments
  • 1 Introduction
    • Ambitions of the twentieth century
    • Pattern classification
    • Prediction and action
    • Chapter notes
  • 2 Fundamentals of Prediction
    • Modeling knowledge
    • Prediction via optimization
    • Types of errors and successes
    • The Neyman-Pearson Lemma
    • Decisions that discriminate
    • Chapter notes
  • 3 Supervised Learning
    • Sample versus population
    • Supervised learning
    • A first learning algorithm: The perceptron
    • Connection to empirical risk minimization
    • Formal guarantees for the perceptron
    • Chapter notes
  • 4 Representations and Features
    • Measurement
    • Quantization
    • Template matching
    • Summarization and histograms
    • Nonlinear predictors
    • Chapter notes
  • 5 Optimization
    • Optimization basics
    • Gradient descent
    • Applications to empirical risk minimization
    • Insights from quadratic functions
    • Stochastic gradient descent
    • Analysis of the stochastic gradient method
    • Implicit convexity
    • Regularization
    • Squared loss methods and other optimization tools
    • Chapter notes
  • 6 Generalization
    • Generalization gap
    • Overparameterization: Empirical phenomena
    • Theories of generalization
    • Algorithmic stability
    • Model complexity and uniform convergence
    • Generalization from algorithms
    • Looking ahead
    • Chapter notes
  • 7 Deep Learning
    • Deep models and feature representation
    • Optimization of deep nets
    • Vanishing gradients
    • Generalization in deep learning
    • Chapter notes
  • 8 Datasets
    • The scientific basis of machine learning benchmarks
    • A tour of datasets in different domains
    • Longevity of benchmarks
    • Harms associated with data
    • Toward better data practices
    • Limits of data and prediction
    • Chapter notes
  • 9 Causality
    • The limitations of observation
    • Causal models
    • Causal graphs
    • Interventions and causal effects
    • Confounding
    • Experimentation, randomization, potential outcomes
    • Counterfactuals
    • Chapter notes
  • 10 Causal Inference in Practice
    • Design and inference
    • The observational basics: Adjustment and controls
    • Reductions to model fitting
    • Quasi-experiments
    • Limitations of causal inference in practice
    • Chapter notes
  • 11 Sequential Decision Making and Dynamic Programming
    • From predictions to actions
    • Dynamical systems
    • Optimal sequential decision making
    • Dynamic programming
    • Computation
    • Partial observation and the separation heuristic
    • Chapter notes
  • 12 Reinforcement Learning
    • Exploration-exploitation trade-offs: Regret and PAC-error
    • Unknown models and approximate dynamic programming
    • Certainty equivalence is often optimal
    • The limits of learning in feedback loops
    • Chapter notes
  • 13 Epilogue
    • Beyond pattern classification?
  • 14 Mathematical Background
    • Common notation
    • Multivariable calculus and linear algebra
    • Probability
    • Estimation
  • Bibliography
  • Index