Classical machine learning is a foundational subfield of artificial intelligence concerned with the development and study of algorithms that enable computer systems to learn from and make predictions or decisions based on data, without explicit programming for specific tasks. It investigates statistical and mathematical methods for pattern recognition, classification, regression, and clustering. Key characteristics include the use of established algorithms like linear models, support vector machines, decision trees, and clustering techniques, often applied to structured data, with a focus on feature engineering and model interpretability, serving as a critical precursor and complement to deep learning methods.