THE MATHEMATICAL FOUNDATIONS OF ARTIFICIAL INTELLIGENCE

Abstract

Artificial Intelligence (AI) stands as a testament to the fusion of mathematics and computer science, where mathematical principles serve as the cornerstone upon which AI algorithms and systems are built. This abstract delves into the intricate relationship between mathematics and AI, exploring how various mathematical disciplines shape and inform the theory and practice of artificial intelligence. Probability theory provides the framework for reasoning under uncertainty, enabling AI systems to make informed decisions in uncertain environments. Linear algebra forms the backbone of data representation and manipulation in machine learning, facilitating tasks such as dimensionality reduction and neural network training. Calculus plays a pivotal role in optimization, driving the iterative process of model parameter tuning and learning from data.