Machine Learning for Engineers

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.

• 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