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
Series:
Chapman Mathematical Notes;
Edition number: 1st ed. 2024
Publisher: Birkhäuser
Date of Publication: 14 June 2024
Number of Volumes: 1 pieces, Book
Normal price:
Publisher's listprice:
EUR 106.99
EUR 106.99
Your price:
35 319 (33 638 HUF + 5% VAT )
discount is: 20% (approx 8 830 HUF off)
Discount is valid until: 30 June 2024
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
Click here to subscribe.
Availability:
Not yet published.
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.