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Machine Learning for Engineers

Autor Osvaldo Simeone
en Limba Engleză Hardback – 2 noi 2022
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.
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Specificații

ISBN-13: 9781316512821
ISBN-10: 1316512827
Pagini: 450
Dimensiuni: 209 x 261 x 38 mm
Greutate: 1.43 kg
Ediția:Nouă
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom

Cuprins

Part I. Introduction and Background: 1. When and how to use machine learning; 2. Background. Part II. Fundamental Concepts and Algorithms: 3. Inference, or model-driven prediction; 4. Supervised learning: getting started; 5. Optimization for machine learning; 6. Supervised learning: beyond least squares; 7: Unsupervised learning. Part III. Advanced Tools and Algorithms: 8. Statistical learning theory; 9. Exponential family of distributions; 10. Variational inference and variational expectation maximization; 11. Information-theoretic inference and learning; 12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning: 13. Transfer learning, multi-task learning, continual learning, and meta-learning; 14. Federated learning. Part V. Epilogue: 15. Beyond this book.

Notă biografică


Descriere

This self-contained introduction contains all students need to start applying machine learning principles to real-world engineering problems.