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Course Criteria
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4.00 Credits
Axioms of probability, joint and conditional probability, random variables, probability density and distribution functions, expectation, functions of random variables, and limit theorems. Applications of probability to models in operations research, including queuing theory and Markov chains. Prerequisites/Corequisites: Prerequisite: MATH 1020 or equivalent or permission of instructor. When Offered: Fall term annually. Cross Listed: Cross-listed as MATP 4600. Students cannot obtain credit for both this course and MATP 4600. Credit Hours: 4
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4.00 Credits
A course in the theory of statistics which will provide students with a basic foundation for more specialized statistical methodology courses. Topics include sampling and sampling distributions; point estimation including method of moments, maximum likelihood estimation, uniform minimum variance estimation and properties of the associated estimators; confidence intervals; hypothesis testing including uniformly most powerful, likelihood ratio approaches, chi-square tests for goodness-of-fit and independence. The course will conclude with an introduction to linear statistical models. Prerequisites/Corequisites: Prerequisite: DSES 4750 or MATP 4600 or equivalent calculus-based course. When Offered: Spring term annually. Cross Listed: Cross-listed as MATP 4620. Students cannot obtain credit for both this course and MATP 4620. Credit Hours: 4
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4.00 Credits
Introduction to deterministic models of operations research including linear programming formulations, the simplex algorithm, degeneracy, geometry of convex polyhedra, duality theory, and sensitivity analysis. Special linear programming models for assignment, transportation, and network problems. Integer programming formulations along with branch and bound solution. Dynamic programming. X Prerequisites/Corequisites: Prerequisites: MATH 1020 and MATH 2010 or ENGR 1100 or equivalent, or permission of instructor. When Offered: Fall term annually. Cross Listed: Cross-listed as MATP 4700. Students cannot obtain credit for both this course and MATP 4700. Credit Hours: 4
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4.00 Credits
An introduction to nonlinear programming. Models, methods, algorithms, and computer techniques for nonlinear optimization are studied. Students investigate contemporary optimization methods both by implementing these methods and through experimentation with commercial software. Nonmajors wishing to gain practical optimization skills are welcome in this course. A course project allows students to explore optimization methods and practical problems directly related to their interests. Prerequisites/Corequisites: Prerequisites: MATP 4700 or DSES 4770, and MATH 2010 or ENGR 1100, and CSCI 1100, or equivalent, or permission of instructor. When Offered: Spring term annually. Cross Listed: Cross-listed as MATP-4820. Students cannot obtain credit for both this course and MATP-4820. Credit Hours: 4
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3.00 Credits
With ever-increasing computer power readily available, new engineering methods based on "soft computing" are emerging at a rapid rate. This course provides students a working knowledge in computational intelligence covering the basics of fuzzy logic, neural networks, genetic algorithms, simulated annealing, wavelet analysis, fractal structures, and chaotic time series analysis. Applications in control, optimization, data mining, fractal image compression, and time series analysis are illustrated with engineering case studies.When Offered: Spring term annually. Credit Hours: 3
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1.00 - 6.00 Credits
Credit Hours: 1 to 6
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3.00 Credits
Credit Hours: 3
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1.00 - 4.00 Credits
Credit Hours: 1 to 4
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3.00 Credits
Emphasis is on empirical model building and evaluation for both multiple linear and nonlinear regression models. Topics specifically addressed are simultaneous estimation, diagnostics and remedial measures, selection procedures, locally weighted least squares classification variables, binary response variables, time series data, nonlinear estimation, software packages. Prerequisites/Corequisites: Prerequisite: DSES 4140, or DSES 4760 (MATP 4620), or DSES 6110, or permission of the instructor. When Offered: Fall term annually. Credit Hours: 3
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3.00 Credits
Methods of designing experiments so that statistical analysis of the resulting data will yield the maximum useful information. Testing of hypotheses; analysis of variance and covariance. Various designs, including the factorial and its modifications, incomplete blocks, Latin squares, and response surface designs are covered. Also discussed are optimality properties of design. Prerequisites/Corequisites: Prerequisites: DSES 4140, or DSES 4750 (MATP 4600) and DSES 4760 (MATP 4620), or DSES 6110, or permission of the instructor. When Offered: Spring term annually. Credit Hours: 3
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