Osvaldo Simeone (King's College London)

Machine Learning for Engineers

• Gebundenes Buch

Produktbeschreibung

Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering problems. With reproducible examples using Matlab, and lecture slides and solutions for instructors, this is the ideal introduction for engineering students of all disciplines.

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Produktdetails

• Verlag: Cambridge University Press
• New ed
• Seitenzahl: 450
• Erscheinungstermin: November 2022
• Englisch
• Abmessung: 260mm x 208mm x 37mm
• Gewicht: 1608g
• ISBN-13: 9781316512821
• ISBN-10: 1316512827
• Artikelnr.: 63515824

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.