AI 230 Mathematical Foundations for Computing
This course introduces the mathematical foundations underlying artificial intelligence
and machine learning, with an emphasis on their role in modern computing systems.
Topics include linear algebra for data representation and transformations, probability
and statistics for uncertainty modeling and inference, optimization methods for learning and decision-making, graph theory for structured data, and selected topics in calculus
relevant to learning algorithms. Students develop the ability to use mathematical
reasoning to analyze algorithms, model data-driven systems, and understand the
theoretical principles behind machine learning methods. The course emphasizes
rigorous problem formulation, quantitative analysis, and the effective application of
mathematics to design correct, efficient, and scalable AI-enabled computing solutions.