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 unsupervised learning. Supervised learning topics cover regression and classification, and unsupervised learning topics cover clustering & principal component analysis. Prior basic statistic skills are needed. Prerequisite: Engineering Undergraduate and APMA 3100 or APMA 3110.

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.

Review of ordinary differential equations, initial/boundary value problems. Linear algebra including systems of linear equations, matrices, eigenvalues, eigenvectors, diagonalization. Solution of partial differential equations that govern physical phenomena in science and engineering by separation by variables, superposition, Fourier series, variation of parameter, d'Alembert's solution. Cross-listed as APMA 6410. Prerequisite: Graduate 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 libraries. Prerequisite: Admission to graduate studies.

Topics vary from year to year and are selected to fill special needs of graduate students.