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
General
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