Linear Algebra and Optimization

Catalog description

This course develops the essential tools of linear algebra and multivariable calculus for applications in data analysis and machine learning. Topics include the geometry of vectors and subspaces, linear transformations, projections, matrix equations, matrix decompositions, eigenvalue and eigenvectors, orthogonality, multivariable functions, gradients, and optimization. Applications include the use of Singular Value Decomposition (SVD) in optimization and dimensionality reduction. The course emphasizes conceptual understanding, geometric intuition, and computational proficiency.