Machine Learning for Engineers

This book provides an accessible overview of machine learning and signal processing, with clear explanations of the differences between state-of-the-art techniques and more classical methods, demonstration of the links between information-theoretical concepts and their practical engineering relevance, and reproducible examples using Matlab. It is the ideal introduction to machine learning for engineering students of all disciplines.

Format: Hardback
Length: 450 pages
Publication date: 03 November 2022
Publisher: Cambridge University Press

This comprehensive self-study guide to machine learning, meticulously crafted with engineers in mind, equips students with the essential skills to apply machine learning principles and algorithms to real-world engineering challenges. With a steadfast focus on the interconnections between estimation, detection, information theory, and optimization, it encompasses:

An accessible overview of the interplay between machine learning and signal processing, serving as a solid foundation for advanced studies;
Clear explanations of the distinctions between state-of-the-art techniques and more traditional methods, empowering students with the knowledge to make informed technique selections;
Demonstrations of the connections between information-theoretical concepts and their practical engineering applications;
Reproducible examples utilizing Matlab, enabling hands-on student experimentation.

Assuming a fundamental grasp of probability and linear algebra, accompanied by lecture slides and solutions for instructors, this serves as an ideal introduction to machine learning for engineering students across all disciplines.

ISBN-13: 9781316512821