Patterns, predictions, and actions

• Verfasst von: Hardt, Moritz [VerfasserIn]
• Verfasst von: Recht, Benjamin [VerfasserIn]
• Titel: Patterns, predictions, and actions
• Titelzusatz: foundations of machine learning
• Verf.angabe: Moritz Hardt and Benjamin Recht
• Verlagsort: Princeton ; Oxford
• Verlag: Princeton University Press
• E-Jahr: 2022
• Jahr: [2022]
• Umfang: xvii, 298 Seiten
• Illustrationen: Illustrationen, Diagramme
• Fussnoten: Includes bibliographical references and index
• ISBN: 978-0-691-23373-4
• Schlagwörter: (s)Maschinelles Lernen / (s)Data Science
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Online-Ausgabe: Hardt, Moritz: Patterns, predictions, and actions. - Princeton : Princeton University Press, 2022 |(DLC)2022002235
• RVK-Notation: ST 301
• Sach-SW: COMPUTERS / Data Science / Machine Learning
• Sach-SW: MATHEMATICS / Probability & Statistics / General+
• K10plus-PPN: 1805466135

Abstract

"An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impactsPatterns, 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. The text: 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, and traces the development of machine learning from its origins to today. Also features a novel chapter on machine learning benchmarks and datasets and invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra. An essential textbook for students and a guide for researchers"