This course aims to teach students the basic math concepts for Artificial Intelligence (AI). Key topics include fundamental Linear Algebra (Matrix Calculations, Norms, Eigenvectors and Eigenvalues), Calculus (Derivative, Taylor series, Multivariate Calculus), and Probability Theory (Distributions, Statistics of Random Variables, Bayes’ theorem). With these mathematical concepts, some basic principles of numerical optimization and typical AI algorithms (Gradient Descent, Maximum-likelihood, Regression, Least Square Estimation, Spectral Clustering, Matrix Decomposition, etc.) will also be introduced as examples to better relate math to AI. The approach of this course is specifically AI application oriented, aiming to help students to quickly establish a fundamental mathematical knowledge structure for AI studies. Through this course, students will acquire the fundamental mathematical concepts required for AI, understand the connections between AI and mathematics, and get prepared to learn the mathematical principles, formulas, inductions, and relevant proofs for advanced AI algorithms.