Applications of Big Data and Machine Learning in Galaxy Formation and Evolution
eBook - ePub

Applications of Big Data and Machine Learning in Galaxy Formation and Evolution

  1. 408 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Applications of Big Data and Machine Learning in Galaxy Formation and Evolution

About this book

As investigations into our Universe become more complex, in-depth, and widespread, galaxy surveys are requiring state-of-the-art data scientific methods to analyze them. This book provides a practical introduction to big data in galaxy formation and evolution, introducing the astrophysical basics, before delving into the latest techniques being introduced to astronomy and astrophysics from data science. This book helps translate the cutting-edge methods into accessible guidance for those without a formal background in computer science. It is an ideal manual for astronomers and astrophysicists, in addition to graduate students and postgraduate students in science and engineering looking to learn how to apply data-science to their research.

Key Features:

  • Introduces applications of data-science methods to the exciting subject of galaxy formation and evolution
  • Provides a practical guide to understanding cutting-edge data-scientific methods, as well as classical astrostatistical methods
  • Summarises a vast range of statistical and informatics methods in one volume, with concrete applications to astrophysics

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Yes, you can access Applications of Big Data and Machine Learning in Galaxy Formation and Evolution by Tsutomu T. Takeuchi in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Computer Science General. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. Section I Physics of Galaxy Structure, Formation, and Evolution
  9. Section II Statistical Approach
  10. Section III Machine Learning Approach
  11. Section IV Appendix
  12. Bibliography
  13. Index