METHODOLOGY OF MACHINE LEARNING IN STATISTICAL ANALYSIS
Ключевые слова:
machine learning, statistical analysis, supervised learning, unsupervised learning, predictive modelingАннотация
Machine learning (ML) has become a revolutionary approach in statistical analysis with improved data interpretation and predictive modeling abilities. In this study, the methodological underpinnings of ML in statistical applications are explored, with approaches like supervised and unsupervised learning, reinforcement learning, and deep learning being highlighted. Through a review of important algorithms, performance metrics, and real-life applications, this study offers interesting perspectives on how ML augments conventional statistical methods. The findings highlight the growing synergy between ML and statistical analysis in favor of advances in data-driven decision-making
Библиографические ссылки
Resolution of the President of the Republic of Uzbekistan No. PP-358 dated 14.10.2024 "On approval of the Strategy for the development of artificial intelligence technologies until 2030".
Decree of the President of the Republic of Uzbekistan - No. UP-157 dated 14.10.2024 "On additional measures to support enterprises engaged in export activities in the field of digitalization".
Babyonyshev, S. V., Malyutin, O. S., & Materov, E. N. (2020). Application of machine learning in higher education student performance prediction. CyberLeninka Journal of Educational Studies.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
Petyunin, S. A., Verbov, D. V., & Lavrinchuk, R. V. (2018). ML-based classification in information system workloads. Information and Mathematical Technologies, (3), 63-72.
Slyusar, V. I. (2022). Multi-channel data analysis in AI-based signal processing. Radiotechnical Systems and Complexes, (4), 45-58.
Yakunin, Y. Y., et al. (2021). A mathematical model for predicting academic performance based on course data. Siberian Federal University Journal of Computational Science.