Product details:

ISBN13:9783031518218
ISBN10:3031518217
Binding:Hardback
No. of pages:694 pages
Size:235x155 mm
Language:English
Illustrations: 5 Illustrations, black & white
700
Category:

Exercises in Applied Mathematics

With a View toward Information Theory, Machine Learning, Wavelets, and Statistical Physics
 
Edition number: 1st ed. 2024
Publisher: Birkhäuser
Date of Publication:
Number of Volumes: 1 pieces, Book
 
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Short description:

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.

Long description:
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 theymove on to more advanced work.
Table of Contents:
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.