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Exercises in Applied Mathematics : With a View toward Information Theory, Machine Learning, Wavelets, and Statistical Physics / by Daniel Alpay
(Chapman Mathematical Notes. ISSN:30051517)

Edition 1st ed. 2024.
Publisher (Cham : Springer International Publishing : Imprint: Birkhäuser)
Year 2024
Language English
Size IX, 694 p. 5 illus : online resource
Authors *Alpay, Daniel author
SpringerLink (Online service)
Subjects LCSH:Algebras, Linear
LCSH:Probabilities
LCSH:Machine learning
LCSH:Harmonic analysis
LCSH:Statistical Physics
FREE:Linear Algebra
FREE:Applied Probability
FREE:Machine Learning
FREE:Abstract Harmonic Analysis
FREE:Statistical Physics
Notes Prologue -- Part I: Algebra -- Linear Algebra -- Positive Matrices -- Algebra and Error Correcting Codes -- Part II: Analysis -- Complements in Real and Complex Analysis -- Complements in Functional Analysis -- Part III: Probability and Applications -- Probability Theory -- Entropy: Discrete Case -- Thermodynamics
This text presents a collection of mathematical exercises with the aim of guiding readers to study topics in statistical physics, equilibrium thermodynamics, information theory, and their various connections. It explores essential tools from linear algebra, elementary functional analysis, and probability theory in detail and demonstrates their applications in topics such as entropy, machine learning, error-correcting codes, and quantum channels. The theory of communication and signal theory are also in the background, and many exercises have been chosen from the theory of wavelets and machine learning. Exercises are selected from a number of different domains, both theoretical and more applied. Notes and other remarks provide motivation for the exercises, and hints and full solutions are given for many. For senior undergraduate and beginning graduate students majoring in mathematics, physics, or engineering, this text will serve as a valuable guide as they move on to more advanced work
HTTP:URL=https://doi.org/10.1007/978-3-031-51822-5
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E-Book オンライン 電子ブック

Springer eBooks 9783031518225
電子リソース
EB00238267

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Material Type E-Book
Classification LCC:QA184-205
DC23:512.5
ID 4001111973
ISBN 9783031518225

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