This course introduces a rich variety of geometry topics beyond those studied at the high school level. Each topic is augmented with connections to the arts, sciences, engineering, and other everyday applications. Course activities will emphasize problem solving in geometry and communicating mathematical arguments in the context of geometry. Geometric concepts will be explored using technology as well.

This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to real-world problems. A statistical package such as Minitab or R is used for data analysis and statistical applications.

This is a second course in linear algebra that provides an in-depth study of fundamental concepts of the subject. It focuses largely on the effect that a choice of basis has on our understanding of and ability to solve problems with linear operators. Topics include linear transformations, similarity, inner products and orthogonality, QR factorization, singular value decomposition, and the Spectral Theorem. The course includes both computational techniques and the further development of mathematical reasoning skills.

This course is an investigation and extension of the theoretical aspects of elementary calculus. Topics include mathematical induction, real numbers, sequences, functions, limits, and continuity. The workshop will focus on helping students develop skill in writing proofs.

This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.

This course serves as a bridge course that builds the mathematical foundations needed for the IDAI-620 course, Mathematical Methods for Artificial Intelligence, a course introducing the mathematical background for AI systems in the MS in AI program. It focuses on the basic constructions, structures, and results in four key areas: (1) linear algebra (vectors, matrices, and their operations) (2) optimization theory (multivariable functions and their calculus) (3) probability and statistics (basic combinatorics, elementary statistics) and (4) numerical analysis (basic notions of approximation).

This course is a study of dynamical systems theory. Basic definitions of dynamical systems are followed by a study of maps and time series. Stability theory of solutions of differential equations is studied. Asymptotic behavior of solutions is investigated through limit sets, attractors, Poincaré–Bendixson theory, and index theory. The notion of local bifurcation is introduced and investigated. Chaotic systems are studied.

Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.

This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.

This course is a graduate course for students enrolled in the Thesis/Project track of the MS Applied Statistics Program. (Enrollment in this course requires permission from the Director of Graduate Programs for Applied Statistics.)

Credit will be assigned at the discretion of the department. A written proposal of the work involved will be required of the candidate, and may be modified at the discretion of the faculty involved before approval is given to proceed.