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The goal of this chapter is to provide an explanation of several well-known mathematical terms that are used in machine learning methods presented in this book. In the first part, we cover basic statistical terms such as standard deviation, variance, coefficient matrix, and Pearson correlation. It is followed by the probability terms and related topics like combinatorics, conditional probability, and probability distribution. The third section, even though it is not very extensive, consists of the crucial part in each machine learning method—operations on matrices. The next section is about differential calculus. To understand what a gradient is, we need to explain a few other terms in the first place. The first term that we explain in this section is the limits.

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Machine Learning Math Basics

  • 章节

  • 发表日期：

    2026年

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  • Karol Przystalski,
  • Maciej J. Ogorzałek,
  • Jan K. Argasiński,
  • Wiesław Chmielnicki

摘要

The goal of this chapter is to provide an explanation of several well-known mathematical terms that are used in machine learning methods presented in this book. In the first part, we cover basic statistical terms such as standard deviation, variance, coefficient matrix, and Pearson correlation. It is followed by the probability terms and related topics like combinatorics, conditional probability, and probability distribution. The third section, even though it is not very extensive, consists of the crucial part in each machine learning method—operations on matrices. The next section is about differential calculus. To understand what a gradient is, we need to explain a few other terms in the first place. The first term that we explain in this section is the limits.