Machine Learning for Engineers

Osvaldo Simeone (King's College London)

$103.95

Hardback

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English

Cambridge University Press
03 November 2022

Information theory; Communications engineering & telecommunications; Machine learning; Pattern recognition; Signal processing

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: an 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; 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.

By: Osvaldo Simeone (King's College London)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Edition: New edition
Dimensions: Height: 261mm, Width: 209mm, Spine: 38mm
Weight: 1.470kg
ISBN: 9781316512821
ISBN 10: 1316512827
Pages: 450
Publication Date: 03 November 2022
Audience: General/trade , ELT Advanced
Format: Hardback
Publisher's Status: Active

Osvaldo Simeone is a Professor of Information Engineering at King's College London, where he directs King's Communications, Learning & Information Processing (KCLIP) lab. He is a Fellow of the IET and IEEE.