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[Information Mathematics for Machine Learning].

Jun'ichi Kotoku1

  • 1Graduate School of Medical Care and Technology, Teikyo University.

Igaku Butsuri : Nihon Igaku Butsuri Gakkai Kikanshi = Japanese Journal of Medical Physics : an Official Journal of Japan Society of Medical Physics
|July 3, 2020
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Summary

This supplement introduces Kullback-Leibler divergence, a core machine learning concept, alongside mutual information. These methods are essential for understanding information theory in data analysis.

Keywords:
KL divergencemachine learningmathematicsmutual information

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Area of Science:

  • Machine Learning
  • Information Theory
  • Medical Physics

Background:

  • The 2019 JSMP Medical Physics Summer School included a machine learning course.
  • Key concepts in machine learning are crucial for modern data analysis.

Purpose of the Study:

  • To introduce Kullback-Leibler divergence and mutual information.
  • To supplement the machine learning course material.

Main Methods:

  • Conceptual introduction of Kullback-Leibler divergence.
  • Explanation of mutual information.

Main Results:

  • The manuscript provides a foundational understanding of these information theory concepts.
  • Readers gain insight into the application of these concepts in machine learning.

Conclusions:

  • Kullback-Leibler divergence and mutual information are fundamental to machine learning.
  • This resource serves as a valuable supplement for students and researchers.