Machine Learning for Engineers
Material type: TextLanguage: English Publication details: Cambridge : Cambridge University Press, c2023Edition: 1stDescription: xxii, 578 p. : illISBN:- 9781316512821
- 620.00285 SIM
Item type | Current library | Shelving location | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
Reference Collection | Reference Section | Reference Section | 620.00285 SIM | 2023-24 | Available | 98523 |
SUMMARY
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.