Machine learning for engineers / Osvaldo Simeone, King's College London.

Format:

Book

Author/Creator:

Simeone, Osvaldo, author.

Language:

English

Subjects (All):

Engineering--Data processing.

Engineering.

Machine learning.

Physical Description:

xxii, 578 pages : illustrations ; 27 cm

Place of Publication:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2023.

Summary:

"This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes : accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study, clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices, demonstration of the links between information-theoretical concepts and their practical engineering relevance, and reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines"-- Provided by publisher.

Notes:

Includes bibliographical references and index.

ISBN:

9781316512821

1316512827

OCLC:

1314119033

Publisher Number:

99993871673