Mar 28, 2024  
Graduate Record 2011-2012 
    
Graduate Record 2011-2012 [ARCHIVED RECORD]

Course Descriptions


 

Statistics

  
  • STAT 5310 - Clinical Trials Methodology


    Studies experimental designs for randomized clinical trials, sources of bias in clinical studies, informed consent, logistics, and interim monitoring procedures (group sequential and Bayesian methods). Prerequisite: A basic statistics course (MATH 3120/5100) or instructor permission.



    Credits: 3
  
  • STAT 5330 - Data Mining


    This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Basic knowledge of R is required. Prerequisites: Concurrent enrollment in STAT 5120 or consent of instructor.



    Credits: 3
  
  • STAT 5430 - Statistical Computing with SAS


    The course covers database management, programming, elementary statistical analysis, and report generation in SAS. Topics include: managing SAS Data Sets; DATA-step programming; data summarization and reporting using PROCs PRINT, MEANS, FREQ, UNIVARIATE, CORR, and REG; elementary graphics; introductions to the Output Delivery System, the SAS Macro language, PROC IML, and PROC SQL. Prerequisites: Introductory statistics course.



    Credits: 3
  
  • STAT 5440 - Introduction to Bayesian Methods


    This introductory, graduate level course will provide an introduction Bayesian methods with emphasis on modeling and applications. The topics to be covered include methods for forming prior distributions such as conjugate and noninformative priors, derivation of posterior and predictive distributions and their moments, and development of Bayesian models including linear regression, generalized linear models and hierarchical models. Prerequisites: At least one semester of mathematical statistics (STAT 3120 or 5190) and one course in linear models (STAT 5120 or equivalent), or instructor permission.



    Credits: 3
  
  • STAT 5559 - New Course in Statistics


    This course provides the opportunity to offer a new topic in the subject area of statistics.



    Credits: 1 to 4
  
  • STAT 5980 - Applied Statistics Laboratory


    This course, the laboratory component of the department’s applied statistics program, deals with the use of computer packages in data analysis. Enrollment in STAT 5980 is required for all students in the department’s 5000-level applied statistics courses (STAT 5010, 5120, 5130, 5140, 5160, 5170, 5200). STAT 5980 may be repeated for credit provided that a student is enrolled in at least one of these 5000-level applied courses; however, no more than one unit of STAT 5980 may be taken in any semester. Corequisite: 5000-level STAT applied statistics course.



    Credits: 1
  
  • STAT 5999 - Topics in Statistics


    Studies topics in statistics that are not part of the regular course offerings. Prerequisite: Instructor permission.



    Credits: 3
  
  • STAT 6120 - Linear Models


    Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression. Prerequisite: MATH 3510, enrollment in graduate program in Statistics or instructor permission; corequisite: STAT 5980.



    Credits: 3
  
  • STAT 7110 - Foundations of Statistics


    Introduction to the concepts of statistics via the establishment of fundamental principles which are then applied to practical problems. Such statistical principles as those of sufficiency, ancillarity, conditionality, and likelihood will be discussed. Prerequisite: STAT 5190 or instructor permission.



    Credits: 3
  
  • STAT 7120 - Statistical Inference


    A rigorous mathematical development of the principles of statistics. Covers point and interval estimation, hypothesis testing, asymtotic theory, Bayesian statistics, and decision theory from a unified perspective. Prerequisite: STAT 7110 or instructor permission.



    Credits: 3
  
  • STAT 7130 - Generalized Linear Models


    Includes the origins of generalized linear models, classical linear models, probit analysis, logit models for proportions, log-linear models for counts, inverse polynomial models, binary data, polytomous data, quasi-likelihood models, and models for survival data. Prerequisite: STAT 5120 and 5190, or instructor permission.



    Credits: 3
  
  • STAT 7140 - Multivariate Statistical Analysis


    Includes multivariate normal distributions, maximum likelihood inference, invariance theory, sample correlation coefficients, Hotelling’s T2 statistic, Wishart distributions, discriminant analysis, and MANOVA. Prerequisite: STAT 5130 and 5190, or instructor permission.



    Credits: 3
  
  • STAT 7150 - Non-Parametric Statistical Analysis


    Includes order statistics, distribution-free statistics, U-statistics, rank tests and estimates, asymtotic efficiency, Bahadur efficiency, M-estimates, one- and two-way layouts, multivariate location models, rank correlation, and linear models. Prerequisite: STAT 5190 and one of STAT 5120, 5130, 5140, 5160, 5170; or instructor permission.



    Credits: 3
  
  • STAT 7180 - Sample Surveys


    Discussion of the main designs and estimation techniques used in sample surveys: simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non response and other non sampling errors. Except for students in their first semester of graduate study, students in the graduate program in Statistics should enroll in STAT 7180. Prerequisites: STAT 5190.



    Credits: 3
  
  • STAT 7190 - Statistical Computing


    Studies computational methods for multiple linear regression, unconstrained optimization and non-linear regression, model-fitting based on Lp norms, and robust estimation. Prerequisite: STAT 5120 and 5180, or instructor permission.



    Credits: 3
  
  • STAT 7200 - Advanced Probability Theory for Applied Scientists


    The course will emphasize those techniques which are important for the applied statistician: various forms of convergence for random variables, central limit theorems, asymptotics for a transformation of a sequence of random variables, and an introduction to martingales. Prerequisite: MATH 5310 or instructor permission.



    Credits: 3
  
  • STAT 7210 - Advanced Linear Models


    Review of matrix theory (various types of generalized inverses and their properties). Theory and analysis of fixed effects linear models. Estimation of variance components in random and mixed effects linear models. Various methods of estimation of variance components such as: Henderson’s three methods, MLE, RMLE, MINQUE (and its modifications). Theory and analysis of random and mixed effects models. Prerequisite: MATH 3510, STAT 5120, 5130, 5190, or instructor permission.



    Credits: 3
  
  • STAT 7220 - Martingale Theory


    An introduction to martingale theory and stochastic differential equations with applications to survival analysis and sequential clinical trials. Prerequisites: STAT 7200 or MATH 7360.



    Credits: 3
  
  • STAT 7310 - Applied Biostatistical Data Analysis


    Includes modern computer-intensive methods of data analysis, including splines and other methods of nonparametric regression, bootstrap, techniques for handling missing values and data reduction, nonlinear regression, graphical techniques, and penalized maximum likelihood estimation. Prerequisite: STAT 5120 and 5130, or instructor permission.



    Credits: 1 to 4
  
  • STAT 7559 - Applied Biostatistical Data Analysis


    The objective is to help students integrate and apply statistical methods learned in other courses to real data from medial research. Students will learn to identifiy the scientific objectives of a study, and develop and implement appropriate strategies. They will present their intermediate and final results in both oral and written forms. This course will prepare the students for a future career as applied statisticians.



    Credits: 1 to 4
  
  • STAT 7950 - Statistical Bioinformatics in Medicine


    Provides an introduction to bioinformatics and discusses important topics in computational biology in medicine, particularly based on modern statistical computing approaches. Reviews state-of-the-art high-throughput biotechnologies, their applications in medicine, and analysis techniques. Requires active student participation in various discussions on the current topics in biotechnology and bioinformatics.



    Credits: 3
  
  • STAT 7995 - Statistical Consulting


    Introduces the practice of statistical consultation. A combination of formal lectures, meetings with clients of the statistical consulting service, and sessions in the statistical computing laboratory. Prerequisite: Current registration in the statistics graduate program, or instructor permission.



    Credits: 1 to 3
  
  • STAT 8120 - Topics in Statistics


    Study of topics in statistics that are currently the subject of active research.



    Credits: 3
  
  • STAT 8170 - Advanced Time Series


    Introduces stationary stochastic processes, related limit theorems, and spectral representations. Includes an asymtotic theory for estimation in both the time and frequency domains. Prerequisite: MATH 7360, STAT 5170, or instructor permission.



    Credits: 3
  
  • STAT 8320 - Topics in Biostatistics


    Study of topics in biostatistics that are currently the subject of active research.



    Credits: 3
  
  • STAT 9120 - Statistics Seminar


    Advanced graduate seminar in current research topics. Offerings in each semester are determined by student and faculty research interests.



    Credits: 3
  
  • STAT 9993 - Directed Reading


    Research into current statistical problems under faculty supervision.



    Credits: 3 to 9
  
  • STAT 9998 - Non-Topical Research, Preparation for Doctoral Research


    For doctoral research, taken before a dissertation director has been selected.



    Credits: 3 to 12
  
  • STAT 9999 - Non-Topical Research


    For doctoral research, taken under the supervision of a dissertation director.



    Credits: 3 to 12

Studies in Women and Gender

  
  • SWAG 5140 - Advanced Border Crossings: Women, Islam, & Lit. in Middle East & N. Africa


    A focus on a bloodless, non-violent revolution that is shaking the foundation of the Islamic Middle East and North Africa, a revolution with women writers at the forefront. An examination of the rhetoric and poetics of sex segregation, voice, visibility, and mobility in a spectrum of genres that includes folklore, novel, short story, poetry, biography, autobiography, and essay. This course section is for graduate students only. Prerequisites: Instructor Consent Required



    Credits: 3

Systems & Information Engineering

  
  • SYS 5044 - Economics of Engineering


    This course is an introduction to the theory of the industrial organization (from a game-theoretic perspective) and its applications to industries with strong engineering content (electricity, telecommunications, software and hardware, etc.). Topics include: congestion pricing in networks, pricing and efficiency in electricity markets, planned obsolescence in software development, “networks” effects and the dynamics of technology adoption. Prerequisite: ECON 201, APMA 310 or 311.



    Credits: 3
  
  • SYS 5581 - Selected Topics in Systems Engineering


    Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.



    Credits: 3
  
  • SYS 6001 - Introduction to Systems Engineering


    An integrated introduction to systems methodology, design, and management. An overview of systems engineering as a professional and intellectual discipline, and its relation to other disciplines, such as operations research, management science, and economics. An introduction to selected techniques in systems and decision sciences, including mathematical modeling, decision analysis, risk analysis, and simulation modeling. Elements of systems management, including decision styles, human information processing, organizational decision processes, and information system design for planning and decision support. Emphasizes relating theory to practice via written analyses and oral presentations of individual and group case studies. Prerequisite: Admission to the graduate program.



    Credits: 3
  
  • SYS 6002 - Systems Integration


    Provides an introduction to the problems encountered when integrating large systems, and also presents a selection of specific technologies and methodologies used to address these problems. Includes actual case-studies to demonstrate systems integration problems and solutions. A term project is used to provide students with the opportunity to apply techniques for dealing with systems integration. Prerequisite: SYS 601 or instructor permission.



    Credits: 3
  
  • SYS 6003 - Mathematical Programming


    This course is an introduction to theory and application of mathematical optimization. The goal of this course is to endow the student with a) a solid understanding of the subject’s theoretical foundation and b) the ability to apply mathematical programming techniques in the context of diverse engineering problems. Topics to be covered include a review of convex analysis (separation and support of sets, application to linear programming), convex programming (characterization of optimality, generalizations), Karush-Kuhn-Tucker conditions, constraint qualification and Lagrangian duality. The course closes with a brief introduction to dynamic optimization in discrete time. Prerequisite: Two years of college mathematics, including linear algebra, and the ability to write computer programs.



    Credits: 3
  
  • SYS 6005 - Stochastic Systems


    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. Prerequisite: APMA 310, 312, or equivalent background in applied probability and statistics.



    Credits: 3
  
  • SYS 6009 - The Art and Science of Systems Modeling


    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.



    Credits: 3
  
  • SYS 6012 - Dynamic Systems


    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. Prerequisite: APMA 213 or equivalent.



    Credits: 3
  
  • SYS 6013 - Applied Multivariate Statistics


    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.



    Credits: 3
  
  • SYS 6014 - Decision Analysis


    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. Prerequisite: SYS 603, 605, or equivalent.



    Credits: 3
  
  • SYS 6016 - Knowledge-Based Systems


    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.



    Credits: 3
  
  • SYS 6018 - Data Mining


    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. Prerequisite: SYS 621, SYS 421, or STAT 512.



    Credits: 3
  
  • SYS 6021 - Linear Statistical Models


    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.



    Credits: 3
  
  • SYS 6023 - Cognitive Systems Engineering


    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).



    Credits: 3
  
  • SYS 6026 - Quantitative Models of Human Perceptual Information Processing


    An introduction to the measurement and modeling of human perceptual information processing, with approaches from neurophysiology to psychophysics, for the purposes of system design. Measurement includes classical psychophysics, EEG field potentials, and single-neuron recordings. Modeling includes signal detection theory, neuronal models (leaky integrate-and-fire, Hodgkin-Huxley, and models utilizing regression, probability, and ODEs). Prerequisities: Graduate standing in Systems and Information Engineering; background courses in ordinary differential equations, statistics and probability; or consent of instructor.



    Credits: 3
  
  • SYS 6034 - Discrete-Event Stochastic Simulation


    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. Prerequisite: SYS 6005 or equivalent background in probability, statistics, and stochastic processes.



    Credits: 3
  
  • SYS 6035 - Agent-Based Modeling and Simulation of Complex Systems


    Complex system are composed of many independent parts, each endowed with behavioral rules that dictate its actions while the collective behavior of the overall system displays unpredictable, /emergent/ properties, thus the whole is indeed more than the sum of its parts. The course will examine the nature of complex systems as observed in many disciplines including biology, physics, economics, political science, ecology, sociology, and engineering systems. Agent-based modeling and simulation will be used as a tool for further understanding such systems. Prerequisite: Agent-Based Modeling and Simulation of Complex Systems.



    Credits: 3
  
  • SYS 6043 - Applied Optimization


    Presents the foundations of mathematical modeling and optimization, with emphasis on problem formulation and solution techniques. Includes applications of linear programs, nonlinear programs, and combinatorial models, as well as a practical introduction to algorithms for solving these types of problems. Topics are illustrated through classic problems such as service planning, operations management, manufacturing, transportation, and network flows. Prerequisites: Two years of college mathematics, including linear algebra, or instructor permission Note: This course cannot be applied toward completing the requirements for an M.S. or Ph.D. in Systems Engineering



    Credits: 3
  
  • SYS 6045 - Applied Probabilistic Models


    The goal of this course is to develop an operational understanding of the basic tools of probabilistic modeling, including (i) a review of undergraduate probability, (ii) introduction to Bernoulli and Poisson processes with applications, (iii) Markov chains and applications, and (iv) limit theorems. Homework and exams will emphasize the use of basic concepts of probability theory in applications. This course cannot be applied toward completing the requirements for an M.S. or Ph.D. in Systems Engineering.



    Credits: 3
  
  • SYS 6050 - Risk Analysis


    A study of technological systems, where decisions are made under conditions of risk and uncertainty. Topics include conceptualization (the nature, perception, and epistemology of risk, and the process of risk assessment and management) systems engineering tools for risk analysis (basic concepts in probability and decision analysis, event trees, decision trees, and multiobjective analysis), and methodologies for risk analysis (hierarchical holographic modeling, uncertainty taxonomy, risk of rare and extreme events, statistics of extremes, partitioned multiobjective risk method, multiobjective decision trees, fault trees, multiobjective impact analysis method, uncertainty sensitivity index method, and filtering, ranking, and management method). Case studies are examined. Prerequisite: APMA 310, SYS 321, or equivalent.



    Credits: 3
  
  • SYS 6054 - Financial Engineering


    Provides an introduction to basic topics in finance from an engineering and modeling perspective. Topics include the theory of interest, capital budgeting, valuation of firms, futures and forward contracts, options and other derivatives, and practical elements of investing and securities speculation. Emphasis is placed on the development and solution of mathematical models for problems in finance, such as capital budgeting, portfolio optimization, and options pricing; also predictive modeling as it is applied in credit risk management. Prerequisite: SYS 603 or equivalent graduate-level optimization course. Students need not have any background in finance or investment.



    Credits: 3
  
  • SYS 6064 - Applied Human Factors Engineering


    This topic covers principles of human factors engineering, understanding and designing systems that take into account human capabilities and limitations from cognitive, physical, and social perspectives. Models of human performance and human-machine interaction are covered as well as methods of design and evaluation. Prerequisite: Basic statistics knowledge (ANOVA, linear regression)



    Credits: 3
  
  • SYS 6070 - Environmental Systems Analysis


    This course focuses on the infrastructure for the provision of drinking water, wastewater/sewage, and solid waste management services in the context of the environmental systems in which they are embedded and the institutional framework within which they must operate. It begins with coverage of the infrastructure design, operation, and maintenance, proceeds to a treatment of the concept of integrated sanitation systems, and then considers the major environmental issues relevant to these services, including global warming, the millenniu development goals, and sustainability. It also includes a study of the common tools in environmental systems analysis: lifecycle assessment, environmental economics, mass and energy balances, benefit-cost analysis, risk analysis, and environmental forecasting. Prerequisite: CHEM 152, PHYS 241.



    Credits: 3
  
  • SYS 6074 - Total Quality Engineering


    Comprehensive study of quality engineering techniques; characterization of Total Quality Management philosophy and continuous improvement tools; statistical monitoring of processes using control charts; and process improvement using experimental design. Prerequisite: Basic statistics or instructor permission.



    Credits: 3
  
  • SYS 6097 - Graduate Teaching Instruction


    For master’s students.



    Credits: 1 to 12
  
  • SYS 6555 - Special Topics in Distance Learning


    Special Topics in Distance Learning



    Credits: 3
  
  • SYS 6581 - Selected Topics in Systems Engineering


    Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.



    Credits: 3
  
  • SYS 6582 - Selected Topics in Systems Engineering


    Detailed study of a selected topic, determined by the current interest of faculty and students. Offered as required.



    Credits: 3
  
  • SYS 6993 - Independent Study


    Detailed study of graduate course material on an independent basis under the guidance of a faculty member.



    Credits: 1 to 12
  
  • SYS 6995 - Supervised Project Research


    Formal record of student commitment to project research under the guidance of a faculty advisor. Registration may be repeated as necessary.



    Credits: 1 to 12
  
  • SYS 7001 - System and Decision Sciences


    Introduction to system and decision science with focus on theoretical foundations and mathematical modeling in four areas: systems (mathematical structures, coupling, decomposition, simulation, control), human inputs (principles from measurement theory and cognitive psychology, subjective probability theory, utility theory), decisions under uncertainty (Bayesian processing of information, Bayes decision procedures, value of information), and decisions with multiple objectives (wholistic ranking, dominance analysis, multiattribute utility theory). Prerequisite: Mathematical analysis and probability theory at an undergraduate level; admission to the graduate program.



    Credits: 3
  
  • SYS 7002 - Case Studies in Systems Engineering


    Under faculty guidance, students apply the principles of systems methodology, design, and management along with the techniques of systems and decision sciences to systems analysis and design cases. The primary goal is the integration of numerous concepts from systems engineering using real-world cases. Focuses on presenting, defending, and discussing systems engineering projects in a typical professional context. Cases, extracted from actual government, industry, and business problems, span a broad range of applicable technologies and involve the formulation of the issues, modeling of decision problems, analysis of the impact of proposed alternatives, and interpretation of these impacts in terms of the client value system. Prerequisite: SYS 601, 603, and 605.



    Credits: 3
  
  • SYS 7005 - Advanced Stochastic Processes


    Provides a non-measure theoretic treatment of advanced topics in the theory of stochastic processes, focusing particularly on denumerable Markov processes in continuous time and renewal processes. The principal objective is to convey a deep understanding of the main results and their proofs, sufficient to allow students to make theoretical contributions to engineering research. Prerequisite: SYS 605 or equivalent.



    Credits: 3
  
  • SYS 7016 - Artificial Intelligence


    In-depth study of major areas considered to be part of artificial intelligence. In particular, detailed coverage is given to the design considerations involved in automatic theorem proving, natural language understanding, and machine learning. Cross-listed as CS 716. Prerequisite: SYS 616 or CS 616.



    Credits: 3
  
  • SYS 7021 - Research Methods in Systems Engineering


    The study of the philosophy, theory, methodology, and applications of systems engineering provides themes for this seminar in the art of reading, studying, reviewing, critiquing, and presenting scientific and engineering research results. Applications are drawn from water resources, environmental, industrial and other engineering areas. Throughout the semester, students make a presentation of a chosen paper, followed by a discussion, critique, evaluation, and conclusions regarding the topic and its exposition. Corequisite: SYS 601, 603, 605, or equivalent.



    Credits: 3
  
  • SYS 7027 - Quantitative Models of Human Judgment and Decision-making


    This course provides an introduction to quantitative methods of measuring human performance in complex systems. The focus of the selected methodologies is based on providing insight into human performance in order to guide design and/or training. Assignments involve applying the methods to a human-machine system problem. If possible the application domain will involve the student’s research area of interest. Competency with regression techniques (e.g. SYS 421 or SYS 618) and statistics/design of experiments preferred.



    Credits: 3
  
  • SYS 7030 - Time Series Analysis and Forecasting


    An introduction to time series analysis and forecasting. Topics include exploratory data analysis for time-correlated data, time series modeling, spectral analysis, filtering, and state-space models. Time series analysis in both the time domain and frequency domain will be covered. Concentration will be on data analysis with inclusion of important theory. Prerequisite: SYS 605 or equivalent, SYS 421 or equivalent.



    Credits: 3
  
  • SYS 7034 - Advanced System Simulation


    Seminar on contemporary topics in discrete-event simulation. Topics are determined by student and faculty interests and may include model and simulation theory, validation, experiment design, output analysis, variance-reduction techniques, simulation optimization, parallel and distributed simulation, intelligent simulation systems, animation and output visualization, and application domains. Term project. Prerequisite: SYS 605, 634, or equivalent.



    Credits: 3
  
  • SYS 7042 - Heuristic Search


    Characterization and analysis of problem solving strategies guided by heuristic information. The course links material from optimization, intelligence systems, and complexity analysis. Formal development of the methods and complete discussion of applications, theoretical properties, and evaluation. Methods discussed include best-first strategies for OR and AND/OR graphs, simulated annealing, genetic algorithms and evolutionary programming, tabu search, and tailored heuristics. Applications of these methods to engineering design, scheduling, signal interpretation, and machine intelligence. Prerequisite: SYS 605 or instructor permission.



    Credits: 3
  
  • SYS 7050 - Risk Analysis


    A study of technological systems, where decisions are made under conditions of risk and uncertainty. Part I: Conceptualization: the nature of risk, the perception of risk, the epistemology of risk, and the process of risk assessment and management. Part II: Systems engineering tools for risk analysis: basic concepts in probability and decision analysis, event trees, decision trees, and multiobjective analysis. Part III: Methodologies for risk analysis: hierarchical holographic modeling, uncertainty taxonomy, risk of rare and extreme events, statistics of extremes, partitioned multiobjective risk method, multiobjective decision trees, fault trees, multiobjective impact analysis method, uncertainty sensitivity index method, and filtering, ranking, and management method. Case studies. Prerequisite: APMA 310, SYS 321, or equivalent.



    Credits: 3
  
  • SYS 7052 - Sequential Decision Processes


    Topics include stochastic sequential decision models and their applications; stochastic control theory; dynamic programming; finite horizon, infinite horizon models; discounted, undiscounted, and average cost models; Markov decision processes, including stochastic shortest path problems; problems with imperfect state information; stochastic games; computational aspects and suboptimal control, including neuro-dynamic programming; examples: inventory control, maintenance, portfolio selection, optimal stopping, water resource management, and sensor management. Prerequisite: SYS 605, 614, or equivalent.



    Credits: 3
  
  • SYS 7054 - Multiobjective Optimization


    Analyzes the theories and methodologies for optimization with multiple objectives under certainty and uncertainty; structuring of objectives, selection of criteria, modeling and assessment of preferences (strength of preference, risk attitude, and trade-off judgments); vector optimization theory and methods for generating non-dominated solutions. Methods with prior assessment of preferences, methods with progressive assessment of preferences (iterative-interactive methods), methods allowing imprecision in preference assessments; group decision making; building and validation of decision-aiding systems. Prerequisite: SYS 603, 614, or equivalent.



    Credits: 3
  
  • SYS 7063 - Response Surface Methods


    Response surface methods provide process and design improvement through the collection and analysis of data from controlled experimentation. This course investigates the construction of response models for systems with discrete and continuous valued responses. The course will cover design of experiments for optimization and methods for building and using response surfaces from simulation, known as simulation-optimization. Prerequisite: SYS 601, 605, and 674, or instructor permission.



    Credits: 3
  
  • SYS 7070 - Sequencing and Scheduling


    A comprehensive treatment of scheduling theory and practice. The formal machine-scheduling problem: assumptions, performance measures, job and flow shops, constructive algorithms for special cases, disjunctive and integer programming formulations, branch-and-bound and dynamic programming approaches, computational complexity and heuristics. Includes alternative scheduling paradigms and scheduling philosophies and software tools in modern applications. Prerequisite: SYS 603, 605, or equivalent.



    Credits: 3
  
  • SYS 7075 - Bayesian Forecast-Decision Theory


    Presents the Bayesian theory of forecasting and decision making; judgmental and statistical forecasting, deterministic and probabilistic forecasting, post-processors of forecasts; sufficient comparisons of forecasters, verification of forecasts, combining forecasts; optimal and suboptimal decision procedures using forecasts including static decision models, sequential decision models, stopping-control models; economic value of forecasts; communication of forecasts; and the design and evaluation of a total forecast-decision system. Prerequisite: SYS 605, 614, or equivalent.



    Credits: 3
  
  • SYS 7096 - Systems Engineering Colloquium


    Regular meeting of graduate students and faculty for presentation and discussion of contemporary systems problems and research. Offered for credit each semester. Registration may be repeated as necessary.



    Credits: 1
  
  • SYS 7097 - Topics in Systems Engineering


    Seminar devoted to a specific topic in Systems Engineering methodology or application, as defined by the instructor. (Note: This course is not to be confused with the more generic Systems Engineering Colloquium (SYS 7096), required for each Systems Engineering degree program.)



    Credits: 1 to 3
  
  • SYS 7555 - Advanced Topics in Distance Learning


    Advanced Topics in Distance Learning



    Credits: 3
  
  • SYS 7581 - Advanced Topics in Systems Engineering


    Detailed study of an advanced or exploratory topic determined by faculty and student interest. Offered as required.



    Credits: 3
  
  • SYS 7582 - Advanced Topics in Systems Engineering


    Detailed study of an advanced or exploratory topic determined by faculty and student interest. Offered as required.



    Credits: 3
  
  • SYS 7993 - Independent Study


    Detailed study of graduate course material on an independent basis under the guidance of a faculty member.



    Credits: 1 to 12
  
  • SYS 8995 - Supervised Project Research


    Formal record of student commitment to project research for Master of Engineering degree under the guidance of a faculty advisor. Registration may be repeated as necessary.



    Credits: 1 to 12
  
  • SYS 8999 - Non-Topical Research, Masters


    Formal record of student commitment to master’s research under the guidance of a faculty advisor. Registration may be repeated as necessary.



    Credits: 1 to 12
  
  • SYS 9997 - Graduate Teaching Instruction


    For doctoral students.



    Credits: 1 to 12
  
  • SYS 9999 - Dissertation


    For doctoral students.



    Credits: 1 to 12

Tibetan

  
  • TBTN 5010 - Advanced Modern Tibetan I


    A continuation of the Intermediate Tibetan I/II sequence, focusing on advanced grammar, syntax, and structures. Emphasis is placed on mastering comprehension and communication in colloquial Tibetan, writing skills in the various scripts of literary Tibetan, and integrating comprehension of colloquial and literary forms. The course employs a dynamic, interactive format to foster speaking and listening skills. Pre-Requisites: TBTN 2020 Intermediate Tibetan II.



    Credits: 3
  
  • TBTN 5030 - Advanced Modern Tibetan III


    A continuation of the Advanced Tibetan I/II language sequence, focusing on advanced grammar, syntax, and structures. Additional emphasis will be placed mastering oral communications skills through conversation, utilizing grammatical structures introduced in Advanced Modern Tibetan II. Pre-Requisites: TBTN 5020 Advanced Modern Tibetan II.



    Credits: 3
  
  • TBTN 5040 - Advanced Modern Tibetan IV


    A continuation of the Advanced Tibetan language sequence, focusing on advanced grammar, syntax, and structures. Additional emphasis will be placed on mastering oral communications skills through conversation, utilizing grammatical structures introduced in previous courses. Pre-Requisites: TBTN 5030 Advanced Modern Tibetan III.



    Credits: 3
  
  • TBTN 5559 - New Course in Tibetan


    New course in Tibetan.



    Credits: 1 to 4

Urban and Environmental Planning

  
  • PLAC 5041 - Advanced Real Estate Development and Finance


    The course will examine the dialogue between economic forces and design decisions in the real estate development process. The course will emphasize the ability of intelligent design to create lasting economic value and the utilization of marketing and finance strategy to augment project viability and profitability.



    Credits: 3
  
  • PLAC 5130 - Applied GIS Workshop


    Students apply GIS technology to examine significant issues of land, natural resources, and the characteristics of urban development.



    Credits: 3
  
  • PLAC 5430 - Land Development Workshop


    Explores the land development process from the perspective of the private land developer interacting with local governments. Includes development potential, site, and traffic analysis; land planning; development programming; and services to accommodate new development and public regulation of land development.



    Credits: 3
  
  • PLAC 5440 - Affordable Housing


    The issue of affordable housing is one that touches every community and which is a major challenge. There are a variety of housing needs that the market does not address effectively with the result that many families pay a disproportionate share of their income for housing while others have long commutes in order to find housing that is affordable.



    Credits: 3
  
  • PLAC 5500 - Topical Offerings in Planning


    Topical Offerings in Planning



    Credits: 3
  
  • PLAC 5610 - Community Planning Workshop


    Explores neighborhood, planning issues from the professionals’ and citizens’ perspectives. Cross-listed with PLAC 5610.



    Credits: 3
  
  • PLAC 5720 - Transportation and Land Use


    Reviews basic relationships between land use and transportation. Considers the decision process, planning principles, impact measures, and the methodological framework for identifying and evaluating practices in action on a regional, local, and neighborhood scale.



    Credits: 3
  
  • PLAC 5740 - Transportation Planning and Policy


    This course introduces graduate and advanced undergraduate students to current issues in the field of transportation planning and policy. It addresses all modes of transportation (auto, walk, bike) and considers multiple scales (national, state, regional and local). Through the analysis of key topics such as congestion, air quality, social equity, and security, we will gain an understanding of how decisions about the transportation system are made and the role of transportation planners and advocates in these decisions.



    Credits: 3
  
  • PLAC 5800 - Green Lands


    This course assesses the existing ‘green infrastructure’ of counties in Virginia and develops strategies for protecting environmental assets and channeling future development to the most appropriate locations. Students will use the existing county comprehensive plan to create effective strategies for implementation of goals related to conserving open space and creating livable communities.



    Credits: 3
  
  • PLAC 5820 - Sustainable Planning and Design Workshop


    Students act as a consultant team to develop sustainable planning and design strategies for sites which rotate each year.



    Credits: 3
  
  • PLAC 5850 - Community Food Systems


    Students will gain experience in policies that support a sustainable food system. They will undertake community projects that span production, distribution, processing, and consumption of food, and also gain practical knowledge in effective community engagement.



    Credits: 3
 

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