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 dierent 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 eects 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-os: 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.