MATHEMATICAL BASIS OF ARTIFICIAL INTELLIGENCE SYSTEMS AND OPTIMIZATION PROCESS

Authors

  • Yunusov Ganisher Gafirovich Associate Professor, Asia International University

DOI:

https://doi.org/10.55640/

Keywords:

x

Abstract

 This article analyzes the mathematical foundations of artificial intelligence (AI) systems and the optimization process. In creating AI models, the stages of data collection, normalization, formation of test and training sets, and optimization of parameters by minimizing the error function are consistently covered. The essence of the gradient method, standardization (Z-score) and the importance of the accuracy and regression coefficient (R²) indicators are also shown.

According to the research results, the effective use of mathematical functions and optimization methods can increase the accuracy, reliability, and speed of SI systems.

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References

1.Bottou , L., Curtis, FE, Nocedal , J. (2018). Optimization for Machine Learning. MIT Press. DOI: 10.7551/ mitpress /8996.001.0001

2.Sra , S., Nowozin , S., Wright, SJ (2012). Optimization for Machine Learning. MIT Press.

3.Aggarwal, CC (2020). Optimization and Machine Learning. Wiley Online Books . DOI: 10.1002/9781119902881

4.Bhatia, S., Singh, A., et al. (2020). Optimization in Machine Learning and Applications. Springer. DOI: 10.1007/978-981-15-0994-0

5.Wheeler, JP (2023). An Introduction to Optimization with Applications in Machine Learning & Data Analytics. Taylor & Francis . Link

6.Suh, C. (2022). Convex Optimization for Machine Learning. Now Publishers . ISBN 978-1-63828-052-1. Link

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Published

2025-11-10

Issue

Vol. 4 No. 10 (2025): Journal of Multidisciplinary Sciences and Innovations

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.

How to Cite

MATHEMATICAL BASIS OF ARTIFICIAL INTELLIGENCE SYSTEMS AND OPTIMIZATION PROCESS. (2025). Journal of Multidisciplinary Sciences and Innovations, 4(10), 429-432. https://doi.org/10.55640/