Книга Machine Learning, revised and updated edition Ethem Alpaydin

Machine Learning, revised and updated edition

Автор: Ethem Alpaydin
Език: Английски език
Корици: С меки корици
Издател: MIT Press Ltd
Наличност: Външен склад
Изпращаме след 9-15 дни
14.55 28.45 лв
A concise overview of machine learning--computer programs that learn from data--the basis of such ap...

Информация за книгата

Автор
Език
Английски език
Корици
Книга - С меки корици
Издадена
2021
страници
280
EAN
9780262542524
ISBN
0262542528
Enbook ID
33448192
Издател
Теглоt
254
Размери
178 x 127 x 16

Пълно описание

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 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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