Mathematical Methods for Data Science II

Undergraduate / Graduate

This course introduces topics in linear algebra for data science students. Topics covered include Vectors spaces, linear transformations and associated subspaces, orthogonality and projections, orthogonal (orthonormal) matrices, eigenvectors and eigenvalues, diagonalization, Det and Tr, matrix decompositions (LU decomposition, singular value decomposition), and transfer matrices and discrete stochastic processes. Assignments and examples focus on results and algorithms that are relevant to machine learning and data science.