Mathematics of AI

Catalog description

This course enables learners to apply advanced mathematics, including calculus, linear algebra, probability, statistics, and optimization, to analyze and solve problems in artificial intelligence engineering. Learners use mathematical reasoning to model, analyze, and evaluate machine learning and deep learning systems, with emphasis on model training dynamics, generalization, stability, and performance. The course develops learners ’ ability to analyze complex AI engineering problems by applying linear algebra techniques such as vector spaces and matrix factorizations, evaluate gradients, loss surfaces, and convergence behavior using multivariable calculus and optimization, and assess uncertainty and performance through probability, statistics, and information theory. Learners conduct computational experiments and data-driven analysis using Python, NumPy, and appropriate machine learning libraries to validate mathematical models and support engineering decisions. Throughout the course, learners demonstrate the ability to interpret results, draw justified conclusions, and communicate mathematical reasoning in the context of AI system design and evaluation.