MATH 352: Mathematics for AI and Data Science

Course Description

The course covers key concepts and techniques in matrix algebra and optimization that are essential for AI and data science. It aims to build the mathematical foundation needed to understand and develop machine learning algorithms widely used in these fields. Topics include vectors, matrices, systems of linear equations, linear independence, rank, norms, orthogonality, projection, eigenvalues, singular value decomposition, basic vector calculus, convex optimization, Lagrange multipliers, gradient descent, and linear regression. The course also introduces Python for numerical computations. No prior programming experience is required. Prerequisite: A grade of C or better in MATH 172, 256 or 301.