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Course Criteria
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3.00 Credits
This course introduces elementary concepts in dynamical systems, including canonical, continuous, and discrete models, equilibria, stability, and bifurcation of solutions of differential equations. Prerequisite/Restriction: MATH 2250 or MATH 2280 with a C- or better
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3.00 Credits
This course covers important topics on ordinary differential equations. Topics include the solution of linear systems of equations, series solutions and the method of Froebenius, and boundary value problems. Prerequisites: MATH 2250 or MATH 2270 with a C- or better MATH 2280 with a C- or better
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3.00 Credits
Elementary point-set topology, topological spaces, separation axioms, metric spaces, compactness, connectedness, order topology, countability axioms, continuity, and homeomorphisms. Prerequisite/Restriction: C- or better in MATH 4200.
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3.00 Credits
Introduces fundamental concepts of financial mathematics, focusing on applications to non-life insurance. Topics include interest theory, cash flows and yield rates, annuities, portfolio insurance, and derivatives. Also includes discussion of probability models for underlying assets. Prerequisite/Restriction: MATH 1220 and STAT 3000. Semester(s) Traditionally Offered: Spring
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3.00 Credits
Introduction to theory of risk and its application to construction and analysis of models for insurance systems. Prerequisite/Restriction: C- or better in MATH 5710, STAT 3000, and permission of instructor. Semester(s) Traditionally Offered: Taught as needed
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3.00 Credits
Continuation of MATH 5570. Prerequisite/Restriction: C- or better in MATH 5570.
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2.00 Credits
Students learn numerical solutions of systems of linear and nonlinear equations, methods for eigensystems, least squares problems, finding roots of functions and nonlinear systems, and constrained and unconstrained optimization. Prerequisite/Restriction: MATH 4610 or equivalent
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2.00 Credits
Students solve initial value problems (IVP) and boundary value problems (BVP) in one dimension using standard methods. Topics include implicit and explicit methods, local and global error, stability, consistency and convergence, predictor-corrector methods and Runge-Kutta schemes, multi-step methods, and finite-difference methods for BVP. Prerequisite/Restriction: MATH 4610 MATH 2250 or 2280 with a C- or better
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3.00 Credits
This course provides an in-depth overview of important mathematical principles and methods that underlie state-of-the art data science, statistical, and machine learning methods with a focus on linear algebra and multivariate calculus and their data science applications. Additional coursework is required for those enrolled in the graduate-level course. Prerequisites/Restrictions: Graduate standing or: MATH 1210 STAT 3000 or MATH 5710 MATH 1220 and MATH 2270 are recommended Experience programming in Python, R, or Matlab is essential for success in the course Cross/Dual listed as: MATH 6645, STAT 5645, STAT 6645
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3.00 Credits
Students study discrete and continuous probability, random variables, the distribution and density function, joint distributions, conditional probabilities and expectations, Bayes' theorem, moments, moment generating functions, inequalities, convergence in probability and distribution, and central limit theorem. Prerequisite(s): MATH 1220 (with a C- or better), or AP Calculus score of 5 on the BC exam Repeatable for credit: No Grade Mode: Standard
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