Machine Learning for Engineers - Couverture rigide

Simeone, Osvaldo

Couverture rigide

ISBN 10 : 1316512827 ISBN 13 : 9781316512821

Editeur : Cambridge University Press, 2022

Synopsis

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.

A propos du livre

À propos de l'auteur

Osvaldo Simeone is a Professor of Information Engineering at King's College London, where he directs King's Communications, Learning & Information Processing (KCLIP) lab. He is a Fellow of the IET and IEEE.

Détails de l'édition

• Éditeur: Cambridge University Press
• Date d'édition: 2022
• Langue: anglais
• ISBN 10: 1316512827
• ISBN 13: 9781316512821
• Reliure: Relié
• Nombre de pages: 450
• Coordonnées du fabricant: non disponible
• Personne responsable: Avery; https://www.avery.fr/; ; chemin des peupliers 27; Dardilly; France