To view prices and purchase online, please login or create an account now.



Machine Learning for Engineers

Hardback

Main Details

Title Machine Learning for Engineers
Authors and Contributors      By (author) Osvaldo Simeone
Physical Properties
Format:Hardback
Pages:450
Dimensions(mm): Height 261,Width 209
Category/GenreCommunications engineering and telecommunications
Signal processing
ISBN/Barcode 9781316512821
ClassificationsDewey:620.00285631
Audience
General
Illustrations Worked examples or Exercises

Publishing Details

Publisher Cambridge University Press
Imprint Cambridge University Press
Publication Date 3 November 2022
Publication Country United Kingdom

Description

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.

Author Biography

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.