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Cover: 9781009218122 | Inference and Learning from Data: Volume 1 | Foundations | Sayed
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Inference and Learning from Data: Volume 1
Foundations
Buch von Ali H. Sayed
Sprache: Englisch

113,95 €*

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Beschreibung
A contemporary textbook and manual for students and aspiring environmental professionals that provides theory and practical skills and concepts needed for today's environmental manager. This foundational textbook provides the scaffolds to allow students to create partnerships to solve environmental problems with successful implementation techniques.
A contemporary textbook and manual for students and aspiring environmental professionals that provides theory and practical skills and concepts needed for today's environmental manager. This foundational textbook provides the scaffolds to allow students to create partnerships to solve environmental problems with successful implementation techniques.
Über den Autor
Ali H. Sayed is Professor and Dean of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He has also served as Distinguished Professor and Chairman of Electrical Engineering at the University of California, Los Angeles, USA, and as President of the IEEE Signal Processing Society. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS), and a recipient of the 2022 IEEE Fourier Award and the 2020 IEEE Norbert Wiener Society Award. He is a Fellow of the IEEE.
Inhaltsverzeichnis
Contents; Preface; Notation; 1. Matrix theory; 2. Vector differentiation; 3. Random variables; 4. Gaussian distribution; 5. Exponential distributions; 6. Entropy and divergence; 7. Random processes; 8. Convex functions; 9. Convex optimization; 10. Lipschitz conditions; 11. Proximal operator; 12. Gradient descent method; 13. Conjugate gradient method; 14. Subgradient method; 15. Proximal and mirror descent methods; 16. Stochastic optimization; 17. Adaptive gradient methods; 18. Gradient noise; 19. Convergence analysis I: Stochastic gradient algorithms; 20. Convergence analysis II: Stochasic subgradient algorithms; 21: Convergence analysis III: Stochastic proximal algorithms; 22. Variance-reduced methods I: Uniform sampling; 23. Variance-reduced methods II: Random reshuffling; 24. Nonconvex optimization; 25. Decentralized optimization I: Primal methods; 26: Decentralized optimization II: Primal-dual methods; Author index; Subject index.
Details
Erscheinungsjahr: 2022
Fachbereich: Kommunikationswissenschaften
Genre: Medienwissenschaften
Rubrik: Wissenschaften
Medium: Buch
Seiten: 1010
Inhalt: Gebunden
ISBN-13: 9781009218122
ISBN-10: 1009218123
Sprache: Englisch
Einband: Gebunden
Autor: Sayed, Ali H.
Hersteller: Cambridge University Press
Maße: 251 x 185 x 48 mm
Von/Mit: Ali H. Sayed
Erscheinungsdatum: 22.12.2022
Gewicht: 1,781 kg
preigu-id: 121612532
Über den Autor
Ali H. Sayed is Professor and Dean of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He has also served as Distinguished Professor and Chairman of Electrical Engineering at the University of California, Los Angeles, USA, and as President of the IEEE Signal Processing Society. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS), and a recipient of the 2022 IEEE Fourier Award and the 2020 IEEE Norbert Wiener Society Award. He is a Fellow of the IEEE.
Inhaltsverzeichnis
Contents; Preface; Notation; 1. Matrix theory; 2. Vector differentiation; 3. Random variables; 4. Gaussian distribution; 5. Exponential distributions; 6. Entropy and divergence; 7. Random processes; 8. Convex functions; 9. Convex optimization; 10. Lipschitz conditions; 11. Proximal operator; 12. Gradient descent method; 13. Conjugate gradient method; 14. Subgradient method; 15. Proximal and mirror descent methods; 16. Stochastic optimization; 17. Adaptive gradient methods; 18. Gradient noise; 19. Convergence analysis I: Stochastic gradient algorithms; 20. Convergence analysis II: Stochasic subgradient algorithms; 21: Convergence analysis III: Stochastic proximal algorithms; 22. Variance-reduced methods I: Uniform sampling; 23. Variance-reduced methods II: Random reshuffling; 24. Nonconvex optimization; 25. Decentralized optimization I: Primal methods; 26: Decentralized optimization II: Primal-dual methods; Author index; Subject index.
Details
Erscheinungsjahr: 2022
Fachbereich: Kommunikationswissenschaften
Genre: Medienwissenschaften
Rubrik: Wissenschaften
Medium: Buch
Seiten: 1010
Inhalt: Gebunden
ISBN-13: 9781009218122
ISBN-10: 1009218123
Sprache: Englisch
Einband: Gebunden
Autor: Sayed, Ali H.
Hersteller: Cambridge University Press
Maße: 251 x 185 x 48 mm
Von/Mit: Ali H. Sayed
Erscheinungsdatum: 22.12.2022
Gewicht: 1,781 kg
preigu-id: 121612532
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