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Machine Learning - The MIT Press Essential Knowledge Series

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Concise introduction to machine learning's evolving landscape.

If you're intrigued by AI and how it's shaping our future, this book offers a streamlined entry point into machine learning without overwhelming you with jargon. Alpaydin's expanded edition doesn't just explain key concepts and algorithms; it also addresses contemporary concerns like privacy and ethical implications, making it relevant for anyone interested in the societal impacts of technology.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.
New

Machine Learning - The MIT Press Essential Knowledge Series

Regular price $9.90
Unit price
per
Compare to estimated retail price: S$23.82  
ISBN: 9780262542524
Authors: Ethem Alpaydin
Publisher: The MIT Press
Date of Publication: 2021-08-17
Format: Paperback
Related Collections: Science, Business
Goodreads rating: 3.62
(rated by 1017 readers)

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A concise overview of machine learning—computer programs that learn from data—the basis of such applications as voice recognition and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition—as well as some we don't yet use every day, including driverless cars. It is the basis for a new approach to artificial intelligence that aims to program computers to use example data or past experience to solve a given problem. In this volume in the MIT Press Essential Knowledge series, Ethem Alpaydin offers a concise and accessible overview of "the new AI." This expanded edition offers new material on such challenges facing machine learning as privacy, security, accountability, and bias. Alpaydin, author of a popular textbook on machine learning, explains that as "Big Data" has gotten bigger, the theory of machine learning—the foundation of efforts to process that data into knowledge—has also advanced. He describes the evolution of the field, explains important learning algorithms, and presents example applications. He discusses the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances; and reinforcement learning, when an autonomous agent learns to take actions to maximize reward. In a new chapter, he considers transparency, explainability, and fairness, and the ethical and legal implications of making decisions based on data.
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Concise introduction to machine learning's evolving landscape.

If you're intrigued by AI and how it's shaping our future, this book offers a streamlined entry point into machine learning without overwhelming you with jargon. Alpaydin's expanded edition doesn't just explain key concepts and algorithms; it also addresses contemporary concerns like privacy and ethical implications, making it relevant for anyone interested in the societal impacts of technology.

Note: While we do our best to ensure the accuracy of cover images, ISBNs may at times be reused for different editions of the same title which may hence appear as a different cover.