Machine Learning: A Taxonomy and Research Roadmap

This technical report presents a comprehensive taxonomy of machine learning methods and a structured roadmap for understanding the field. It organizes machine learning into major learning paradigms, model families, computational mechanisms, task formulations, and deployed systems. The report covers supervised, unsupervised, and semi-supervised learning, probabilistic and statistical learning, kernel methods, ensemble methods, reinforcement learning, optimization, and causal inference. By providing a systematic classification of these approaches and their relationships, the report aims to support students, researchers, and practitioners in navigating the machine learning landscape and identifying directions for further study and research.