MATH 315 Advanced Linear Algebra with Applications to Data Science
This course presents the mathematical structure of vector spaces and multilinear transformations. The axioms of vector spaces are introduced along with the notion of basis and dimension. Properties of dual spaces and multilinear transformations are studied. Eigenvalues and eigenvectors theorems are used to diagonalize matrices. Adjoint, self-adjoint, normal and unitary operators on pre-Hilbert spaces are constructed. Applications of these concepts to data science are emphasized throughout the course.
Prerequisite
MATH204, or MATH211
Distribution
3-0-3Offered
Fall Spring