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Course Criteria
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3.00 - 12.00 Credits
For doctoral research, taken before a dissertation director has been selected.
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3.00 - 12.00 Credits
For doctoral dissertation, taken under the supervision of a dissertation director.
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4.00 Credits
The concepts of differential and integral calculus are developed and applied to the elementary functions of a single variable. Limits, rates of change, derivatives, and integrals. Applications are made to problems in analytic geometry and elementary physics. For students with no exposure to high school calculus.
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4.00 Credits
Includes the concepts of differential and integral calculus and applications to problems in geometry and elementary physics, including inverse functions, indeterminate forms, techniques of integration, parametric equations, polar coordinates, infinite series, including Taylor and Maclaurin series. Applications.
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3.00 Credits
Introduces discrete mathematics and proof techniques involving first order predicate logic and induction. Application areas include sets (finite and infinite, such as sets of strings over a finite alphabet), elementary combinatorial problems, and finite state automata. Develops tools and mechanisms for reasoning about discrete problems. Cross-listed as CS 202.
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4.00 Credits
Topics include vectors in three-space and vector valued functions. The multivariate calculus, including partial differentiation, multiple integrals, line and surface integrals, and the vector calculus, including Green’s theorem, the divergence theorem, and Stokes’s theorem. Applications.
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4.00 Credits
First order differential equations, second order and higher order linear differential equations, reduction of order, undetermined coefficients, variation of parameters, series solutions, Laplace transforms, linear systems of first order differential equations and the associated matrix theory, numerical methods. Applications.
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3.00 Credits
Analyzes the systems of linear equations; vector spaces; linear dependence; bases; dimension; linear mappings; matrices; determinants; quadratic forms; eigenvalues; eigenvectors; orthogonal reduction to diagonal form; inner product spaces; numerical methods; geometric applications.
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3.00 Credits
A calculus-based introduction to probability theory and its applications in engineering and applied science. Includes counting techniques, conditional probability, independence, discrete and continuous random variables, probability distribution functions, expected value and variance, joint distributions, covariance, correlation, the Central Limit theorem, the Poisson process, an introduction to statistical inference.
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3.00 Credits
Introduces computation theory including grammars, finite state machines and Turing machines; and graph theory. Cross-listed as APMA 302.
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