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- List of Figures
- List of Tables
- Preface
- Acknowledgments
- 1 Introduction
- Ambitions of the twentieth century
- Pattern classication
- 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 rst 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 scientic 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 tting
- 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-ös: 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 classication?
- 14 Mathematical Background
- Common notation
- Multivariable calculus and linear algebra
- Probability
- Estimation
- Bibliography
- Index
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Anwendungs-Software |
| Genre: | Importe, Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: | Einband - fest (Hardcover) |
| ISBN-13: | 9780691233734 |
| ISBN-10: | 069123373X |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: |
Recht, Benjamin
Hardt, Moritz |
| Hersteller: | Princeton University Press |
| Verantwortliche Person für die EU: | Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de |
| Maße: | 258 x 179 x 25 mm |
| Von/Mit: | Benjamin Recht (u. a.) |
| Erscheinungsdatum: | 18.10.2022 |
| Gewicht: | 0,73 kg |
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