About the book
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.
Editions of Machine Learning, revised and updated edition
- Paperback
- ISBN 9780262542524
- EBook
- ISBN 9780262365352
Read an Excerpt
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.
Frequently Asked Questions
What is Machine Learning, revised and updated edition about?
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.
What core themes, tropes, or subjects are explored in Machine Learning, revised and updated edition?
Computing and Information Technology > Computer science > Artificial intelligence (AI) > Machine learning
Where can I read a sample of Machine Learning, revised and updated edition?
You can read an official preview of the few pages here https://www.book2look.com/book/9780262542524
Is Machine Learning, revised and updated edition part of a series, and can it be read as a standalone?
Yes it is a part of series The MIT Press Essential Knowledge series
Who is/are the Author/s of the book Machine Learning, revised and updated edition?
Ethem Alpaydin
Who is the Publisher of the book Machine Learning, revised and updated edition?
MIT Press
What are the ISBN numbers for the physical and digital editions?
Machine Learning, revised and updated edition is available as paperback(ISBN 9780262542524) and ebook(ISBN 9780262365352)
Where can I buy Machine Learning, revised and updated edition online or support local independent bookshops?
You can buy Machine Learning, revised and updated edition from below
Where can I buy Machine Learning, revised and updated edition?