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Machine Learning for Engineers
Hardback
Main Details
Title |
Machine Learning for Engineers
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Authors and Contributors |
By (author) Osvaldo Simeone
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Physical Properties |
Format:Hardback | Pages:450 | Dimensions(mm): Height 261,Width 209 |
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Category/Genre | Communications engineering and telecommunications Signal processing |
ISBN/Barcode |
9781316512821
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Classifications | Dewey:620.00285631 |
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Audience | |
Illustrations |
Worked examples or Exercises
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Publishing Details |
Publisher |
Cambridge University Press
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Imprint |
Cambridge University Press
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Publication Date |
3 November 2022 |
Publication Country |
United Kingdom
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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.
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