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Machine Learning for Engineers

Authors

Osvaldo Simeone
, King's College London
Published 2022

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,…

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Key features

  • A book on machine learning written for engineers, by an engineer
  • An accessible text with a unified information-theoretic framework
  • Highlights connections between machine learning and estimation, detection, information theory, and optimization
  • Offers concise but extensive coverage of state-of-the-art topics with simple, reproducible examples
  • Derives modern methods, such as generative adversarial networks, from first principles, revealing their connection with standard techniques
  • Divided into useful parts, allowing the book easily to be mapped to either a one- or a two-semester course

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