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 …
Advanced techniques of integration are introduced, and integration is used in physics applications like fluid force, work, and center of mass. Improper integrals and approximate integration using Simpson's Rule are …
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 …
First order differential equations, second order and higher order linear differential equations, undetermined coefficients, variation of parameters, Laplace transforms, linear systems of first order differential equations and the associated matrix …
Analyze and apply systems of linear equations; vector spaces; linear transformations; matrices; determinants; eigenvalues; eigenvectors; coordinates; diagonalization; orthogonality; projections; inner product spaces; quadratic forms; The course is both computational and …
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 …
Introduces basic concepts of probability such as random variables, single and joint probability distributions, and the central limit theorem. The course then emphasizes applied statistics, including descriptive statistics, statistical inference, …
Includes point estimation methods, confidence intervals, hypothesis testing for one population and two populations, categorical data tests, single and multi-factor analysis of variance (ANOVA) techniques, linear and non-linear regression and …
Partial differential equations that govern physical phenomena in science and engineering. Separation of variables, superposition, Fourier series, Sturm-Liouville eigenvalue problems, eigenfunction expansion techniques. Particular focus on the heat, wave, and …
This course uses a Case-Study approach to teach statistical techniques with R: confidence intervals, hypotheses tests, regression, and anova. Also, it covers major statistical learning techniques for both supervised and …
Topics include analytic functions, Cauchy Theorems and formulas, power series, Taylor and Laurent series, complex integration, residue theorem, conformal mapping, and Laplace transforms. Prerequisite: APMA 2120 or MATH 2310 or …
Applies mathematical techniques to special problems of current interest. Topic for each semester are announced at the time of course enrollment.
Reading and research under the direction of a faculty member. Prerequisite: Fourth-year standing.
Analyzes the role of statistics in science; hypothesis tests of significance; confidence intervals; design of experiments; regression; correlation analysis; analysis of variance; and introduction to statistical computing with statistical software …
Topics vary from year to year and are selected to fill special needs of graduate students.