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Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)

Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)

Machine Learning, revised and updated edition (The MIT Press Essential Knowledge
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Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series) Paperback - 2021

by Alpaydin, Ethem

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The MIT Press, 8/17/2021 12:00:01 A. paperback. Like New. 1.0600 9.2500 6.4200.
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Details

  • Title Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)
  • Author Alpaydin, Ethem
  • Binding Paperback
  • Condition New
  • Pages 280
  • Volumes 1
  • Language ENG
  • Publisher The MIT Press
  • Publication date 8/17/2021 12:00:01 A
  • Features Bibliography, Glossary, Index
  • Bookseller's Inventory # mon0003965679
  • ISBN 9780262542524 / 0262542528
  • Weight 0.55 lbs (0.25 kg)
  • Dimensions 6.9 x 5 x 0.7 in (17.53 x 12.70 x 1.78 cm)
  • Size 1.0600 9.2500 6.4200
  • Age range 18 to UP years
  • Grade levels 13 - UP
  • Category Computers - General Information
  • Library of Congress subjects Artificial intelligence, Machine learning
  • Library of Congress Catalogue Number 2020033697
  • Dewey Decimal Code 006.31
  • Quantity available 31
  • Bookseller catalogues Book

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From the publisher

A concise primer on machine learning--computer programs that learn from data and the basis of applications like voice recognition and driverless cars.

No in-depth knowledge of math or programming required!

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 explains that as Big Data has grown, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. He covers:

- The evolution of machine learning
- Important learning algorithms and example applications
- Using machine learning algorithms for pattern recognition
- Artificial neural networks inspired by the human brain
- Algorithms that learn associations between instances
- Reinforcement learning
- Transparency, explainability, and fairness in machine learning
- The ethical and legal implicates of data-based decision making

A comprehensive introduction to machine learning, this book does not require any previous knowledge of mathematics or programming--making it accessible for everyday readers and easily adoptable for classroom syllabi.

About the author

Ethem Alpaydn is Professor in the Department of Computer Engineering at zyegin University and a member of the Science Academy, Istanbul. He is the author of the widely used textbook, Introduction to Machine Learning (MIT Press), now in its fourth edition.

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