Foundations of Machine Learning Algorithms: Evolution from Classical to Modern Methods

Qianze Chai

doi:10.54254/2755-2721/2025.AST25837

Applied and Computational EngineeringOpen access

Foundations of Machine Learning Algorithms: Evolution from Classical to Modern Methods

Research Article

Open Access

Foundations of Machine Learning Algorithms: Evolution from Classical to Modern Methods

Qianze Chai ^1*

¹ Taiyuan University of Technology

^*Corresponding author: chaiqianze@qq.com

Published on 6 August 2025

ACE Vol.175

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-237-9

ISBN (Online): 978-1-80590-238-6

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Abstract

With the advent of the big data era and the rapid advancement of computing power, machine learning has become the core driving force behind the development of artificial intelligence. From medical diagnosis to face recognition payment, and from autonomous driving to intelligent recommendation systems, machine learning algorithms have deeply penetrated various sectors of society. Traditional machine learning algorithms, such as Support Vector Machines and Decision Trees, established the theoretical foundations of the field, while breakthroughs in modern machine learning—particularly the rise of deep learning and the advent of Transformer architectures—have significantly expanded its frontiers. This paper adopts a research methodology combining literature analysis and review to systematically studies the evolutionary path of fundamental machine learning algorithms from traditional to modern approaches. Through historical combing and comparative analysis, it aims to uncover the underlying logic of machine learning algorithm evolution. The study finds that the evolution of algorithms is the result of a synergy between theoretical innovation and engineering demands.

Keywords:

Machine Learning, Deep Learning, Transformer

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References

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References

[2]. Kang Tongxi. Comparative Analysis of Linear Regression and Logistic Regression [J]. Fujian Quality Managem, 2018(21): 205. DOI: 10.3969/j.issn.1673-9604.2018.21.159.

[3]. Xu Hongxue, Sun Wanyou, Du Yingkui, et al. A Survey of Classic Machine Learning Algorithms and Their Applications [J]. Computer Knowledge and Technology, 2020, 16(33): 17-19.

Cite this article

Chai,Q. (2025). Foundations of Machine Learning Algorithms: Evolution from Classical to Modern Methods. Applied and Computational Engineering,175,58-64.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Application of Machine Learning in Engineering

ISBN: 978-1-80590-237-9(Print) / 978-1-80590-238-6(Online)

Editor: Marwan Omar, Mian Umer Shafiq

Conference website: https://www.confcds.org

Conference date: 19 August 2025

Series: Applied and Computational Engineering

Volume number: Vol.175

ISSN: 2755-2721(Print) / 2755-273X(Online)

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