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

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 learningparticularly the rise of deep learning and the advent of Transformer architectureshave 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.