Math
MATH 280 Math for Data Science (4)
An Introduction to the basic mathematical theory that underlie current data science methods. Students will gain an appreciation for the value of the mathematical theory as well as their limitations. Topics covered in the course will include: 1) Linear modeling and matrix computation (e.g., matrix algebra and factorization, eigenvalues/eigenvectors, and projection/least squares), 2) Optimization (e.g., calculus concepts related to differentiation), 3) Multivariate thinking (e.g., concepts and numerical computation of multivariate derivatives and integrals), and 4) Probabilistic thinking and modeling (e.g., counting principles, univariate and multivariate distributions, and independence). The connection between the mathematical theory and data science applications will be emphasized and the presentation of the theory will be driven by specific data science models and algorithms.