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Patterns, Predictions, and Actions
Foundations of Machine Learning
Buch von Benjamin Recht (u. a.)
Sprache: Englisch

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Beschreibung
"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"--
"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"--
Über den Autor
Moritz Hardt is a director at the Max Planck Institute for Intelligent Systems. Benjamin Recht is professor of electrical engineering and computer sciences at the University of California, Berkeley.
Details
Erscheinungsjahr: 2022
Fachbereich: Anwendungs-Software
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 320
Inhalt: Gebunden
ISBN-13: 9780691233734
ISBN-10: 069123373X
Sprache: Englisch
Einband: Gebunden
Autor: Recht, Benjamin
Hardt, Moritz
Hersteller: Princeton University Press
Maße: 258 x 179 x 25 mm
Von/Mit: Benjamin Recht (u. a.)
Erscheinungsdatum: 18.10.2022
Gewicht: 0,73 kg
preigu-id: 120967992
Über den Autor
Moritz Hardt is a director at the Max Planck Institute for Intelligent Systems. Benjamin Recht is professor of electrical engineering and computer sciences at the University of California, Berkeley.
Details
Erscheinungsjahr: 2022
Fachbereich: Anwendungs-Software
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 320
Inhalt: Gebunden
ISBN-13: 9780691233734
ISBN-10: 069123373X
Sprache: Englisch
Einband: Gebunden
Autor: Recht, Benjamin
Hardt, Moritz
Hersteller: Princeton University Press
Maße: 258 x 179 x 25 mm
Von/Mit: Benjamin Recht (u. a.)
Erscheinungsdatum: 18.10.2022
Gewicht: 0,73 kg
preigu-id: 120967992
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