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Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)
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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 everyday, 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 Alpaydín 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.
Alpaydín, 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.
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 everyday, 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 Alpaydín 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.
Alpaydín, 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.
カテゴリー:
年:
2021
版:
Revised, Updated
出版社:
The MIT Press
言語:
english
ページ:
280
ISBN 10:
0262542528
ISBN 13:
9780262542524
ファイル:
EPUB, 698 KB
あなたのタグ:
IPFS:
CID , CID Blake2b
english, 2021
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