Machine Learning for the Physical Sciences
eBook - ePub

Machine Learning for the Physical Sciences

Fundamentals and Prototyping with Julia

  1. 266 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Machine Learning for the Physical Sciences

Fundamentals and Prototyping with Julia

About this book

Machine learning is an exciting topic with a myriad of applications. However, most textbooks are targeted towards computer science students. This, however, creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields.

This textbook bridges this gap, providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences, without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering, targeting senior undergraduate and graduate students in physics and electrical engineering, alongside advanced researchers.

All codes are available on the author's website: C•Lab (nau.edu)

They are also available on GitHub: https://github.com/StxGuy/MachineLearning

Key Features:

  • Includes detailed algorithms.
  • Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences.
  • All algorithms are presented with a good mathematical background.

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Yes, you can access Machine Learning for the Physical Sciences by Carlo Requião da Cunha in PDF and/or ePUB format, as well as other popular books in Informatique & Biologie. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2023
eBook ISBN
9781003821168
Edition
1
Topic
Informatique
Subtopic
Biologie

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Title Page
  4. Copyright Page
  5. Dedication Page
  6. Contents
  7. Preface
  8. List of Figures
  9. List of Table
  10. Section I Foundations
  11. Section II Unsupervised Learning
  12. Section III Supervised Learning
  13. Section IV Neuronal-Inspired Learning
  14. Section V Reinforcement Learning
  15. Section VI Optimization
  16. Appendix A Sufficient Statistic
  17. Appendix B Graphs
  18. Appendix C Sequential Minimization Optimization
  19. Appendix D Algorithmic Differentiation
  20. Appendix E Batch Normalizing Transform
  21. Appendix F Divergence of two Gaussian Distributions
  22. Appendix G Continuous-time Bellman's Equation
  23. Appendix H Conjugate Gradient
  24. Appendix I Importance Sampling
  25. References
  26. Index