Machine Learning for Engineers
- Textbook
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
About the book
- DOI https://doi.org/10.1017/9781009072205
- Subjects Communications and Signal Processing,Computer Science,Engineering,Machine Learning and Pattern Recognition
- Format: Hardback
- Publication date: 03 November 2022
- ISBN: 9781316512821
- Dimensions (mm): 254 x 203 mm
- Weight: 1.47kg
- Page extent: 450 pages
- Availability: Available
- Format: Digital
- Publication date: 25 January 2023
- ISBN: 9781009072205
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