Machine Learning, Revised and Updated Edition

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 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, 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.

Author: Ethem Alpaydin
Publisher: MIT Press
Published: 08/17/2021
Pages: 280
Binding Type: Paperback
Weight: 0.55lbs
Size: 6.90h x 5.00w x 0.70d
ISBN: 9780262542524

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.