This course, with MATH 1080 forms a two term introduction to numerical analysis at the advanced undergraduate level and includes interpolation, numerical differentiation and integration, solution of non-linear equations, numerical solution of systems or ordinary differential equations, and additional topics as time permits. Emphasis is on understanding the algorithms rather than on detailed coding, although some programming will be required.

This course is an introduction to numerical linear algebra which addresses numerical methods for solving linear algebraic systems and matrix Eigen problems and applications to partial differential equations. Although the course will stress a computational viewpoint, analysis of the convergences and stability of the algorithms will be investigated.

Topics covered will include linear programming problems, the simplex method, quality, revised simplex method, and the transportation problem.

This course introduces students to the techniques of optimization. Applications will be emphasized, but some theory will be addressed and proofs will be discussed. As well, students will be taught how to use available software to answer questions. Course topics will include linear programming, integer programming, nonlinear programming, convex and affine sets, convex and concave functions, unconstrained optimization, and combinatorial optimization (i.e. Network flow problems).

This course presents contemporary mathematical theories of neuroscience, including single neurons and neuronal networks. Attention will be given 1451 to the dynamics and the function of neural activity.

The concept of a graph and the study of its theoretical properties and applications form the core of this course. Topics include paths, circuits, trees, planar graphs, coloring problems, digraphs, matching theory, and network flows.