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

Alpaydin, Ethem

ISBN 10: 0262542528 / ISBN 13: 9780262542524

Published by The MIT Press (edition Updated), 2021

About this Item

It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Seller Inventory # 0262542528-8-1

Bibliographic Details

• Title: Machine Learning, revised and updated edition (The MIT Press Essential Knowledge series)
• Publisher: The MIT Press (edition Updated)
• Publication Date: 2021
• Language: English
• ISBN 10: 0262542528
• ISBN 13: 9780262542524
• Binding: Paperback
• Edition: Updated.
• Condition: Very Good

About this title

Synopsis

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 Alpaydín is Professor in the Department of Computer Engineering at Özyegin 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.