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Course Criteria
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3.00 Credits
Covers basic stochastic processes with emphasis on model building and probabilistic reasoning. The approach is non-measure theoretic but otherwise rigorous. Topics include a review of elementary probability theory with particular attention to conditional expectations; Markov chains; optimal stopping; renewal theory and the Poisson process; martingales. Applications are considered in reliability theory, inventory theory, and queuing systems.
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3.00 Credits
Focuses on learning and practicing the art and science of systems modeling through diverse case studies. Topics span the modeling of discrete and continuous, static and dynamic, linear and non-linear, and deterministic and probabilistic systems. Two major dimensions of systems modeling are discussed and their efficacy is demonstrated: the building blocks of mathematical models and the centrality of the state variables in systems modeling, including: state variables, decision variables, random variables, exogenous variables, inputs and outputs, objective functions, and constraints; and effective tools in systems modeling, including multiobjective models, influence diagrams, event trees, systems identification and parameter estimation, hierarchical holographic modeling, and dynamic programming.
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3.00 Credits
Introduces modeling, analysis, and control of dynamic systems, using ordinary differential and difference equations. Emphasizes the properties of mathematical representations of systems, the methods used to analyze mathematical models, and the translation of concrete situations into appropriate mathematical forms. Primary coverage includes ordinary linear differential and difference equation models, transform methods and concepts from classical control theory, state-variable methods and concepts from modern control theory, and continuous system simulation. Applications are drawn from social, economic, managerial, and physical systems. Cross-listed as MAE 652.
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3.00 Credits
The theory and applications of primary methods for multivariate data analysis, such as MANOVA, principal components, factor analysis, canonical correlation, and discriminant analysis, are covered in this course. Students are expected to be familiar with at least one statistical software package and with concepts of linear algebra. It is cross-listed as STAT 513. Prerequisites: SYS 618, SYS 421/621, or STAT 512 (or their equivalents); courses in linear algebra and univariate statistics; or instructor permission.
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3.00 Credits
Principles and procedures of decision-making under uncertainty and with multiple objectives. Topics include representation of decision situations as decision trees, influence diagrams, and stochastic dynamic programming models; Bayesian decision analysis, subjective probability, utility theory, optimal decision procedures, value of information, multiobjective decision analysis, and group decision making.
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3.00 Credits
A graduate-level survey of artificial intelligence techniques with emphasis on their application to systems engineering problem- solving. Topics include: informed and uninformed search; propositional and first order logic; and learning techniques such as Bayes nets, reinforcement learning and neural networks. Students are required to have sufficient computational background to complete several substantive programming assignments. Cross-listed as CS 616.
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3.00 Credits
Data mining describes approaches to turning data into information. Rather than the more typical deductive strategy of building models using known principles, data mining uses inductive approaches to discover the appropriate models. These models describe a relationship between a system’s response and a set of factors or predictor variables. Data mining in this context provides a formal basis for machine learning and knowledge discovery. This course investigates the construction of empirical models from data mining for systems with both discrete and continuous valued responses. It covers both estimation and classification, and explores both practical and theoretical aspects of data mining.
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3.00 Credits
This course shows how to use linear statistical models for analysis in engineering and science. The course emphasizes the use of regression models for description, prediction, and control in a variety of applications. Building on multiple regression, the course also covers principal component analysis, analysis of variance and covariance, logistic regression, time series methods, and clustering. Course lectures concentrate on theory and practice.
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3.00 Credits
Introduces the field of cognitive systems engineering, which seeks to characterize and support human-systems integration in complex systems environments. Covers key aspects of cognitive human factors in the design of information support systems. Reviews human performance (memory, learning, problem-solving, expertise and human error); characterizes human performance in complex, socio-technical systems, including naturalistic decision making and team performance; reviews different types of decision support systems, with a particular focus on representation aiding systems; and covers the human-centered design process (task analysis, knowledge acquisition methods, product concept, functional requirements, prototype, design, and testing).
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3.00 Credits
A first graduate course covering the theory and practice of discrete-event stochastic simulation. Coverage includes Monte Carlo methods and spreadsheet applications, generating random numbers and variates, specifying input probability distributions, discrete-event simulation logic and computational issues, review of basic queueing theory, analysis of correlated output sequences, model verification and validation, experiment design and comparison of simulated systems, and simulation optimization. Emphasis includes state-of-the-art simulation programming languages with animation on personal computers. Applications address operations in manufacturing, distribution, transportation, communication, computer, health care, and service systems.
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