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005			20250411165126.0
008			250409b |||||||| |||| 00| 0 eng d
020			_a9780691233734
037			_cTextual
040			_aRTL _cRTL
084			_aD6,9(B) R2 _qRTL
100			_aHardt, Moritz _9752054
245			_aPatterns, predictions, and actions: Foundations of machine learning
260			_aPrinceton _bPrinceton University Press _c2022
300			_axvii, 298 p. : ill. _bIncludes bibliographical references and index
520			_aAn 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.
650			_aData Science _9725373
650			_aProbability & Statistics
650			_aComputer
700			_aRecht, Benjamin _eCo-author _9752055
942			_2CC _n0 _cTB _hD6,9(B) R2
999			_c1308623 _d1308623