This course encompasses matrix theory and linear algebra, focusing on concepts applicable across various disciplines. Linear algebra is a branch of mathematics that studies systems of linear equations and the properties of matrices. The course covers the theory of vector spaces and linear transformations as well as practical methods like diagonalization and row-reduction of matrices that can be used to solve issues in mathematical biology, engineering, and economics. It finds widespread use in machine learning for solving linear regression, eigenvalue problems, orthogonalization, matrix inversion and enhancing numerical stability in various algorithms.
