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  • 3.00 Credits

    Description: Application of principles of probability and statistics to the design and control of engineering systems in a random or uncertain environment. Emphasis is placed on Bayesian decision analysis. Graduate-level requirements include a semester research project. Grading: Regular grades are awarded for this course: A B C D E. Credit for: 1 unit engineering science, 2 units engineering design. May be convened with: SIE 422. Usually offered: Fall.
  • 3.00 Credits

    Description: Application of the theory of stochastic processes to queuing phenomena; introduction to semi-Markov processes; steady-state analysis of birth-death, Markovian, and general single- and multiple-channel queuing systems. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): SIE 520. Usually offered: Spring.
  • 3.00 Credits

    Description: Statistical methodology of estimation, testing hypotheses, goodness-of-fit, nonparametric methods and decision theory as it relates to engineering practice. Significant emphasis on the underlying statistical modeling and assumptions. Graduate-level requirements include additionally more difficult homework assignments. Grading: Regular grades are awarded for this course: A B C D E. May be convened with: SIE 430. Usually offered: Fall.
  • 3.00 Credits

    Description: Discrete event simulation, model development, statistical design and analysis of simulation experiments, variance reduction, random variate generation, Monte Carlo simulation. Graduate-level requirements include a library research report. Grading: Regular grades are awarded for this course: A B C D E. Credit for: 1.5 units engineering science, 1.5 units engineering design. May be convened with: SIE 431. Usually offered: Fall, Spring.
  • 3.00 Credits

    Description: Principles for identifying parametric time series models from discrete data and relationship to autovariance, spectrum, and the Green's function from linear system theory are considered. Theory is developed for application to prediction characterization, signature analysis, and process identification and control. The applications of these theories include precision engineering, experimental mode analysis, process monitoring and diagnosis, quality control, analysis of machining operations, etc. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): SIE 305, SIE 350. Usually offered: Fall.
  • 3.00 Credits

    Description: Planning and designing experiments with an emphasis on factorial layout. Includes analysis of experimental and observational data with multiple linear regression and analysis of variance. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): SIE 530. Usually offered: Spring.
  • 3.00 Credits

    Description: Survey of methods including network flows, integer programming, nonlinear programming, and dynamic programming. Model development and solution algorithms are covered. Graduate-level requirements include additional assigned readings and a project paper. Grading: Regular grades are awarded for this course: A B C D E. Special course fee required: Special fee may apply for web delivered sections. See the M.Eng Website (http://www.oneflexibledegree.com) for details. Credit for: 3 units engineering science. May be convened with: SIE 440. Usually offered: Spring.
  • 3.00 Credits

    Description: Principles of game theory. Historical context, Nash equilibrium, normal form and extensive forms. Stackelberg equilibrium, subgame perfect equilibrium. Cooperative games: core, bargaining, MCDM, social choice, Bayesian games. Examples from engineering, economics, military, national security, and environmental protection. Graduate-level requirements include more advanced homework, exams and projects. Grading: Regular grades are awarded for this course: A B C D E. May be convened with: SIE 443. Usually offered: Fall.
  • 3.00 Credits

    Description: Linear and integer programming formulations, simplex method, geometry of the simplex method, sensitivity and duality, projective transformation methods. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): SIE 340. Usually offered: Fall.
  • 3.00 Credits

    Description: Unconstrained and constrained optimization problems from a numerical standpoint. Topics include variable metric methods, optimality conditions, quadratic programming, penalty and barrier function methods, interior point methods, successive quadratic programming methods. Grading: Regular grades are awarded for this course: A B C D E. Prerequisite(s): SIE 340. Usually offered: Spring.
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