Patterns, Predictions, and Actions: Foundations of Machine Learning

Patterns,Predictions,and Actions is an authoritative and up-to-date graduate textbook on machine learning that provides a comprehensive introduction to the field,its historical context,and societal impacts. It covers key concepts such as representation,optimization,generalization,causality,sequential decision-making,and reinforcement learning,and equips readers with the tools they need to assess the consequences of acting on statistical decisions.

Format: Hardback
Length: 320 pages
Publication date: 13 December 2022
Publisher: Princeton University Press

Patterns, Predictions, and Actions: An Authoritative and Up-to-Date Graduate Textbook on Machine Learning

Machine learning has emerged as a powerful tool in the field of artificial intelligence, revolutionizing the way we analyze and interpret data. This comprehensive textbook, written by Moritz Hardt and Benjamin Recht, aims to introduce graduate students to the essentials of machine learning while also providing valuable insights into its historical context and societal impacts.

The book begins by delving into the foundations of decision-making, emphasizing the importance of representation, optimization, and generalization in supervised learning. The authors then explore various topics such as causality, causal inference, sequential decision-making, and reinforcement learning, equipping readers with the concepts and tools necessary to assess the consequences of acting on statistical decisions.

One of the key strengths of Patterns, Predictions, and Actions is its focus on societal implications and fairness in decision-making. The authors recognize the importance of ensuring that machine learning algorithms are transparent, accountable, and unbiased. They discuss various approaches to addressing fairness concerns, including counterfactual analysis, diversity and inclusion, and adversarial examples.

The book also traces the development of machine learning from its origins to the present day, highlighting key milestones and advancements. It features a novel chapter on machine learning benchmarks and datasets, providing readers with a comprehensive reference for evaluating the performance of different machine learning algorithms.

Designed for students with a background in probability, calculus, and linear algebra, Patterns, Predictions, and Actions is an essential textbook for those pursuing advanced studies in machine learning. It offers a comprehensive and up-to-date overview of the field, covering both theoretical and practical aspects. The authors' clear and concise writing style makes the material accessible to a wide range of readers, including researchers and practitioners alike.

In conclusion, Patterns, Predictions, and Actions is a highly recommended textbook for anyone interested in machine learning. It provides a comprehensive introduction to the field, while also offering valuable perspectives on its historical context and societal impacts. With its emphasis on fairness, transparency, and accountability, the book is a valuable resource for students and researchers alike.

Dimension: 254 x 178 (mm)
ISBN-13: 9780691233734