Patterns, Predictions, and Actions : Foundations of Machine Learning

Benjamin Recht

ISBN 10: 069123373X / ISBN 13: 9780691233734

Edité par Princeton University Press Okt 2022, 2022

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780691233734

Détails bibliographiques

• Titre: Patterns, Predictions, and Actions : Foundations of Machine Learning
• Éditeur: Princeton University Press Okt 2022
• Date de parution: 2022
• Langue: anglais
• ISBN à 10 chiffres: 069123373X
• ISBN à 13 chiffres: 9780691233734
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 258 L x 179 H x 25 P (en millimètres) 258 millimètres de largeur par 179 millimètres de hauteur par 25 millimètres de profondeur
• Poids: 730 grammes

A propos de ce titre

Synopsis

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

À propos de l?auteur

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.