Machine Learning, Revised and Updated Edition
| AUTHOR | Alpaydin, Ethem; Alpaydin, Ethem |
| PUBLISHER | MIT Press (08/17/2021) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
MIT presents 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.
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.
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Product Format
Product Details
ISBN-13:
9780262542524
ISBN-10:
0262542528
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
280
Carton Quantity:
52
Product Dimensions:
5.00 x 0.70 x 6.90 inches
Weight:
0.55 pound(s)
Feature Codes:
Bibliography,
Index,
Price on Product,
Glossary
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Artificial Intelligence - General
Computers | Data Science - Machine Learning
Computers | History
Grade Level:
College Freshman
and up
Dewey Decimal:
006.31
Library of Congress Control Number:
2020033697
Descriptions, Reviews, Etc.
publisher marketing
MIT presents 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.
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.
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