Machine Learning for Engineers

by Osvaldo Simeone

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.

• Format: Hardcover
• Language: English
• ISBN: 1316512827
• ISBN13: 9781316512821
• Release Date: November 2022
• Publisher: Cambridge University Press
• Length: 450 Pages
• Weight: 3.15 lbs.
• Dimensions: 1.3" x 8.0" x 10.0"