Metric spaces, limits, continuous maps and homeomorphisms, connectedness, compact topological spaces and applications.
Topics from differential and integral calculus with applications to the social, behavioral or life sciences and business or management. May be used for general education credit.
A second course in linear algebra, focusing on topics that are the most essential for data science. Introduces theory and numerical methods required for large data-sets and machine learning. Topics include LU, QR, and singular-value decompositions; conditioning and stability; the DFT and filters; deep learning; fully connected and convolutional nets.
Linear equations, matrices, vector spaces, linear transformations, determinants, eigenvalues and inner-product spaces. Includes both theory and computational skills. Develops the ability to reason through and coherently write proofs of theorems. For math majors, this course serves as a transition from a study of techniques into more conceptual math; for engineering and science majors, it serves as a coherent foundation in linear algebra.
First and second order equations, series, Laplace transforms, linear algebra, eigenvalues, first order systems of equations, numerical techniques.
Vector functions, partial differentiation, cylindrical and spherical coordinates, multiple integrals, line integrals, Green's theorem.
Limits, continuity, differentiation and integration of elementary functions with applications.
Limits, continuity, differentiation, and integration of elementary functions with applications in the biosciences.
Transcendental functions, integration techniques, polar coordinates, sequences and series, with mathematical software.