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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

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Patterns, predictions, and actions : foundations of machine learning 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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알라딘제공

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 patterns in data 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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