Foundations of Artificial Intelligence

Catalog description

This course will cover mathematical foundations of AI and machine learning not covered by Calculus I, Calculus II, and Linear Algebra. Topics include p-norm and vector space; partial differentiation and mathematical optimization with Lagrange multipliers. Probability theory will cover discrete/continuous random variables and distribution functions; joint distributions and conditional distributions; expectation; law of large numbers and central limit theorem. Pre-requisites: MATH 2414 ,(Calculus II), and MATH 2318 (Linear Algebra).