Patterns, Predictions, and Actions: Foundations of Machine Learning

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

ISBN-13: 9780691233734

Media Type: Hardcover

Publisher: Princeton University Press

Publication Date: 10-18-2022

Pages: 320

Product Dimensions: 7.00(w) x 10.00(h) x (d)

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.

What People are Saying About This

From the Publisher

“This modern treatment of machine learning is notable for its coverage of emerging, important topics, from datasets and deep learning to optimization, causal inference, and social context, along the way pointing out the attendant perils that come from flawed predictions.”—David C. Parkes, Harvard University

“Patterns, Predictions, and Actions is not your everyday machine learning book; it’s rigorous not only in its mathematics but in its insistence on thinking about what the machines are really doing and what the subject is really about.”—Jordan Ellenberg, author of Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else

“Like Hansel and Gretel, machine learning students find themselves in an intellectual forest, struggling to digest a morass of ephemeral ideas. Hardt and Recht use history, theory, and society to expose the topology of this landscape. Patterns, Predictions, and Actions is a new foundational text for the field.”—Neil Lawrence, University of Cambridge

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Table of Contents

List of Figures xi

List of Tables xiii

Preface xv

Acknowledgments xix

1 Introduction 1

Ambitions of the twentieth century 2

Pattern classification 4

Prediction and action 7

Chapter notes 9

2 Fundamentals of Prediction 11

Modeling knowledge 13

Prediction via optimization 16

Types of errors and successes 20

The Neyman-Pearson Lemma 22

Decisions that discriminate 26

Chapter notes 30

3 Supervised Learning 33

Sample versus population 33

Supervised learning 34

A first learning algorithm: The perceptron 37

Connection to empirical risk minimization 38

Formal guarantees for the perceptron 41

Chapter notes 46

4 Representations and Features 49

Measurement 50

Quantization 51

Template matching 52

Summarization and histograms 53

Nonlinear predictors 54

Chapter notes 65

5 Optimization 69

Optimization basics 70

Gradient descent 71

Applications to empirical risk minimization 74

Insights from quadratic functions 76

Stochastic gradient descent 78

Analysis of the stochastic gradient method 84

Implicit convexity 87

Regularization 90

Squared loss methods and other optimization tools 94

Chapter notes 96

6 Generalization 99

Generalization gap 99

Overparameterization: Empirical phenomena 100

Theories of generalization 105

Algorithmic stability 109

Model complexity and uniform convergence 115

Generalization from algorithms 118

Looking ahead 123

Chapter notes 123

7 Deep Learning 125

Deep models and feature representation 126

Optimization of deep nets 128

Vanishing gradients 134

Generalization in deep learning 137

Chapter notes 141

8 Datasets 143

The scientific basis of machine learning benchmarks 144

A tour of datasets in different domains 145

Longevity of benchmarks 156

Harms associated with data 164

Toward better data practices 169

Limits of data and prediction 172

Chapter notes 173

9 Causality 175

The limitations of observation 176

Causal models 178

Causal graphs 182

Interventions and causal effects 184

Confounding 186

Experimentation, randomization, potential outcomes 189

Counterfactuals 192

Chapter notes 197

10 Causal Inference in Practice 199

Design and inference 200

The observational basics: Adjustment and controls 201

Reductions to model fitting 202

Quasi-experiments 206

Limitations of causal inference in practice 209

Chapter notes 211

11 Sequential Decision Making and Dynamic Programming 213

From predictions to actions 214

Dynamical systems 214

Optimal sequential decision making 216

Dynamic programming 217

Computation 220

Partial observation and the separation heuristic 225

Chapter notes 230

12 Reinforcement Learning 231

Exploration-exploitation trade-offs: Regret and PAC-error 232

Unknown models and approximate dynamic programming 241

Certainty equivalence is often optimal 248

The limits of learning in feedback loops 253

Chapter notes 259

13 Epilogue 261

Beyond pattern classification? 264

14 Mathematical Background 265

Common notation 265

Multivariable calculus and linear algebra 265

Probability 267

Estimation 272

Bibliography 275

Index 295

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