Machine Learning for Engineers (Hardcover, New edition)

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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	November 2022
Authors:	Osvaldo Simeone
Dimensions:	261 x 209 x 38mm (L x W x T)
Format:	Hardcover
Pages:	450
Edition:	New edition
ISBN-13:	978-1-316-51282-1
Categories:	Books > Computing & IT > Applications of computing > Signal processing Books > Computing & IT > Applications of computing > Pattern recognition Books > Reference & Interdisciplinary > Communication studies > Information theory > General Books > Professional & Technical > Electronics & communications engineering > Communications engineering / telecommunications > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-316-51282-7
Barcode:	9781316512821

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