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Patterns, Predictions, and Actions: Foundations of Machine Learning
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 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
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 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
Kindle Edition
Published August 23, 2022
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Displaying 1 - 2 of 2 reviews
April 2, 2021
If you like machine learning, you have to read this one. It's a modern introduction including a lot of novel ideas on generalization and optimization, as long as two chapters on causal inference and more references and historical footnotes that I can count. Way too dense at times, but it can benefit both novices and experienced readers at the same time.
April 16, 2023
Refreshing read, I enjoyed this one a lot. Even after five years in the field -and two years of PhD- I learned from the common threads and links being made between the various chapters.
Edit in April 2023: current author list on Goodreads only mentions Moritz Hardt. I think also Benjamin Recht should be listed.
Edit in April 2023: current author list on Goodreads only mentions Moritz Hardt. I think also Benjamin Recht should be listed.
Displaying 1 - 2 of 2 reviews