Patterns, Predictions, and Actions
eBook - PDF

Patterns, Predictions, and Actions

Foundations of Machine Learning

  1. 320 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Patterns, Predictions, and Actions

Foundations of Machine Learning

About this book

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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Yes, you can access Patterns, Predictions, and Actions by Moritz Hardt,Benjamin Recht in PDF and/or ePUB format. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Contents
  3. List of Figures
  4. List of Tables
  5. Preface
  6. Acknowledgments
  7. 1. Introduction
  8. 2. Fundamentals of Prediction
  9. 3. Supervised Learning
  10. 4. Representations and Features
  11. 5. Optimization
  12. 6. Generalization
  13. 7. Deep Learning
  14. 8. Datasets
  15. 9. Causality
  16. 10. Causal Inference in Practice
  17. 11. Sequential Decision Making and Dynamic Programming
  18. 12. Reinforcement Learning
  19. 13. Epilogue
  20. 14. Mathematical Background
  21. Bibliography
  22. Index