Basic Concepts of Machine Learning

This chapter provides a systematic introduction to the fundamental concepts of machine learning. It first clarifies the relationships among artificial intelligence, machine learning, and deep learning, and reviews the historical development and core ideas of artificial intelligence. Key concepts in machine learning, including data partitioning, feature scaling, robustness, generalization, and overfitting, are then introduced to establish a theoretical foundation for model training and evaluation. The chapter further presents the classification of machine learning from the perspectives of task types and learning paradigms, covering regression, classification, clustering, dimensionality reduction, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Finally, the chapter discusses the application of machine learning in drug development, summarizes common machine learning methods used in drug design, and outlines the general workflow for constructing predictive models, providing a conceptual basis for subsequent chapters.