Patterns, Predictions, and Actions

Name: Patterns, Predictions, and Actions
Brand: Princeton University Press
SKU: 9780691233734
Price: 367.46 RON
Availability: InStock

Foundations of Machine Learning

Autor: Moritz Hardt, Benjamin Recht

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Legare: Copertă tare

Editura: Princeton University Press

Disponibilitate: În depozitul extern

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An authoritative, up-to-date graduate textbook on machine learning that highlights its historical co...

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Autor

Moritz Hardt, Benjamin Recht

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2022

Pagini

320

EAN

9780691233734

ISBN

069123373X

Enbook ID

38458154

Editura

Princeton University Press

Greutate

726

Dimensiuni

178 x 254 x 26

Descriere completă

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. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actionsPays special attention to societal impacts and fairness in decision makingTraces the development of machine learning from its origins to todayFeatures a novel chapter on machine learning benchmarks and datasetsInvites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebraAn essential textbook for students and a guide for researchers

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Patterns, Predictions, and Actions

Foundations of Machine Learning

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