Evolution of Learning Algorithms: A Journey from Traditional to Deep Machine Learning

Abstract

The progression of machine learning algorithms from traditional methods to deep learning represents a profound evolution in artificial intelligence. This journey reflects a shift from rule-based systems and statistical models, reliant on manual feature engineering, to hierarchical neural networks capable of automatic representation learning. Traditional learning algorithms like decision trees, support vector machines, and k-nearest neighbors laid the groundwork by formalizing the process of learning from data. However, their performance was constrained by computational limits and the need for domain expertise in feature extraction. The emergence of deep learning, particularly driven by advances in neural network architectures and computational power, has enabled the development of models that excel in tasks involving unstructured data such as images, text, and audio. This paper traces the historical development of learning algorithms, highlights the milestones that enabled the transition to deep learning, and explores the comparative strengths, applications, and limitations of both paradigms in the broader landscape of machine intelligence.