Model theory and machine learning: points of intersection

Abstract

This paper explores the key points of intersection between model theory—a branch of mathematical logic concerned with the interpretation of formal languages—and machine learning techniques. It examines the potential of using tools from model theory to formalize data models and learning algorithms, thereby enabling a deeper understanding of the expressive power and limitations of data-driven models. Particular attention is paid to generalization, formal verification, correctness of inference, as well as the formalization of environments and interaction rules in reinforcement learning tasks. The contribution of model theory to the development of hybrid Қарағанды, 2025 Пәнаралық ғылыми зерттеулердiң ѳзектi мәселелерi 129 and explainable artificial intelligence models is also discussed. The findings demonstrate that integrating model-theoretic approaches into machine learning opens new opportunities for building more robust, interpretable, and formally grounded intelligent systems.