Osvaldo Simeone

Machine Learning for Engineers (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 25. Januar 2023
• Englisch
• ISBN-13: 9781009080026
• Artikelnr.: 68995016

Autorenporträt

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.

Inhaltsangabe

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.