Issue 2 (208), article 2


Cybernetics and Computer Engineering, 2022, 2(208)

Savchenko-Synyakova Ye.A., PhD (Engineering),
Senior Researcher of the Department for Information
Technologies of Inductive Modeling

International Research and Training Center
for Information Technologies and Systems
of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine


Introduction. Nowadays, the application of machine learning methods and tools is developing very rapidly, given the overall automation and digitalization. The use of machine learning methods and tools for modeling complex processes makes it possible to solve problems that were previously difficult or impossible to solve.

However other methods of mathematical modeling also make it possible to solve the problem of constructing a model based on a sample of experimental data. The task was to compare various scientific areas of artificial intelligence, such as machine learning, mathematical modeling, statistics, data mining and inductive modeling in terms of building mathematical models, to find out what common and distinctive features they have.

The purpose of the paper is a comparative analysis of the areas of mathematical modeling, statistics and machine learning. And also compare the methods of inductive modeling and inductive generation of models.

Results. A comparative analysis of machine learning and other approaches to solving artificial intelligence problems was carried out.

Conclusion. The conducted analysis showed that the machine learning tasks of mathematical (statistical) modeling are close, but not the same, and it is difficult to draw a hard line between them. They can be distinguished by the purpose, the ability to check or interpret the obtained results.

Keywords: machine learning, mathematical modeling, statistics, inductive approach, inductive generation of models.

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Received 22.04.2022