May 17, 2024  
Graduate Record 2013-2014 
    
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Graduate Record 2013-2014 [ARCHIVED RECORD]

Course Descriptions

 

Spanish
  

  • SPAN 7860 - Regional Literature

    Regional Literature



    Credits: 3
  

  • SPAN 7880 - Novel: Twentieth-Century Spanish America

    Novel: Twentieth-Century Spanish America



    Credits: 3
  

  • SPAN 7890 - Essay: Twentieth-Century Spanish America

    Essay: Twentieth-Century Spanish America



    Credits: 3
  

  • SPAN 8210 - Teaching Foreign Languages

    This course provides graduate students teaching foreign languages at UVA with the opportunity to observe and apply new ideas and teaching principles through practical activities and to develop their own personal theories of teaching through systematic reflection and experimentation.



    Credits: 3
  

  • SPAN 8505 - Seminars: Middle Ages and Early Renaissance

    Seminars: Middle Ages and Early Renaissance



    Credits: 3
  

  • SPAN 8510 - Seminars: Golden Age

    Seminars: Golden Age



    Credits: 3
  

  • SPAN 8515 - Seminars: Golden Age

    Seminars: Golden Age



    Credits: 3
  

  • SPAN 8520 - Seminars: Enlightenment to Romanticism

    Seminars: Enlightenment to Romanticism



    Credits: 3
  

  • SPAN 8530 - Seminars: Realism and the Generation of 1898

    Seminars: Realism and the Generation of 1898



    Credits: 3
  

  • SPAN 8540 - Seminars: Modern Spanish Literature

    Seminars: Modern Spanish Literature



    Credits: 3
  

  • SPAN 8550 - Seminars: Spanish America: Colonial Period to 1900

    Seminars: Spanish America: Colonial Period to 1900



    Credits: 3
  

  • SPAN 8560 - Seminars: Spanish America: Modern Period

    Seminars: Spanish America: Modern Period



    Credits: 3
  

  • SPAN 8565 - Seminars: Spanish America: Modern Period

    Seminars: Spanish America: Modern Period



    Credits: 3
  

  • SPAN 8995 - Guided Research

    Readings and/or research in particular fields under the supervision of an instructor.



    Credits: 3
  

  • SPAN 8998 - Non-Topical Research, Preparation for Research

    For master’s research, taken before a thesis director has been selected.



    Credits: 1 to 12
  

  • SPAN 8999 - Non-Topical Research

    For master’s thesis, taken under the supervision of a thesis director.



    Credits: 1 to 12
  

  • SPAN 9995 - Guided Research

    Readings and/or research in particular fields under the supervision of an instructor.



    Credits: 3
  

  • SPAN 9998 - Non-Topical Research, Preparation for Doctoral Research

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



    Credits: 1 to 12
  

  • SPAN 9999 - Non-Topical Research

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



    Credits: 1 to 12

Statistics
  

  • STAT 5000 - Introduction to Applied Statistics

    Introduces estimation and hypothesis testing in applied statistics, especially the medical sciences. Measurement issues, measures of central tendency and dispersion, probability, discrete probability distributions (binomial and Poisson), continuous probability distributions (normal, t, chi-square, and F), and one- and two-sample inference, power and sample size calculations, introduction to non-parametric methods, one-way ANOVA and multiple comparisons. Prerequisite: Instructor permission; corequisite: STAT 5980.



    Credits: 3
  

  • STAT 5020 - Mathematical Statistics

    A calculus based introduction to the principles of statistical inference. Topics include sampling theory, point estimation, confidence intervals, hypothesis testing. Additional topics such as nonparametric methods or Bayesian statistics. May not be used for graduate degrees in Statistics. May not be taken if credit has been received for STAT 3120. Prerequisites: MATH 3100 or 5100 or consent of instructor.



    Credits: 3
  

  • STAT 5120 - Applied 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. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite:STAT 3120, and either MATH 3351 or APMA 3080



    Credits: 4
  

  • STAT 5140 - Survival Analysis and Reliability Theory

    Topics include lifetime distributions, hazard functions, competing-risks, proportional hazards, censored data, accelerated-life models, Kaplan-Meier estimator, stochastic models, renewal processes, and Bayesian methods for lifetime and reliability data analysis. Prerequisite: MATH 3120 or 5100, or instructor permission; corequisite: STAT 5980.



    Credits: 3
  

  • STAT 5150 - Actuarial Statistics

    Covers the main topics required by students preparing for the examinations in Actuarial Statistics, set by the American Society of Actuaries. Topics include life tables, life insurance and annuities, survival distributions, net premiums and premium reserves, multiple life functions and decrement models, valuation of pension plans, insurance models, and benefits and dividends. Prerequisite: MATH 3120 or 5100, or instructor permission.



    Credits: 3
  

  • STAT 5170 - Applied Time Series

    Studies the basic time series models in both the time domain (ARMA models) and the frequency domain (spectral models), emphasizing application to real data sets. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: STAT 3120



    Credits: 4
  

  • STAT 5180 - Design and Analysis of 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. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisites: STAT 3120.



    Credits: 4
  

  • STAT 5190 - Introduction to Mathematical Statistics

    Studies statistical distribution theory, moments, transformations of random variables, point estimation, hypothesis testing, and confidence regions. Prerequisite: MATH 3120 or 5100, or instructor permission.



    Credits: 3
  

  • STAT 5260 - Categorical Data Analysis

    The course covers topics in categorical data, including contingency tables, generalized linear models, logistic regression, and logit and loglinear models. Prerequisite: STAT 3120.



    Credits: 3
  

  • STAT 5265 - Investment Science I

    The course will cover a broad range of topics, with the overall theme being the quantitative modeling of asset allocation and portfolio theory. It begins with deterministic cash flows (interest theory, fixed-income securities), the modeling of interest rates (term structure of interest rates), stochastic cash flows, mean-variance portfolio theory, capital asset pricing model, and the utility theory basis for financial modeling. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using Matlab or R statistical software. Prerequisite: MATH 3100.



    Credits: 4
  

  • STAT 5266 - Investment Science II

    This course is a follow-up to Investment Science I (Stat 5265). It begins with models for derivative securities, including asset dynamics, options and interest rate derivatives. The remaining portion of the course then combines all of the ideas from the two courses to formulate strategies of optimal portfolio growth and a general theory of investment evaluation. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using Matlab or R statistical software. Prerequisite: MATH 3100, STAT 5265.



    Credits: 4
  

  • 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. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisites: Previous or concurrent enrollment in STAT 5120 or STAT 6120.



    Credits: 4
  

  • STAT 5340 - Bootstrap and Other Resampling Methods

    This course introduces the basic ideas of resampling methods, from jackknife and the classic bootstrap due to Efron to advanced bootstrap techniques such as the estimating function bootstrap and the Markov chain marginal bootstrap.



    Credits: 3
  

  • STAT 5410 - Introduction to Statistical Software

    This course develops basic data skills in SAS and R, focusing on data-set management and the production of elementary statistics. Topics include data input, cleaning and reshaping data, producing basic statistics, and simple graphics. The student is prepared for the development of advanced data-analysis techniques in applied statistics courses.



    Credits: 1
  

  • 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 5510 - Contemporary Topics in Statistics

    This course exposes students to new data types and emerging topics in statistical methodology and computation, emphasizing literacy and applied data-analysis. Topics vary by instructor.



    Credits: 1
  

  • 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

    Course develops fundamental methodology to regression and linear-models analysis in general. Topics include model fitting and inference, partial and sequential testing, variable selection, transformations, diagnostics for influential observations, multicollinearity, and regression in nonstandard settings. Conceptual discussion in lectures is supplemented withhands-on practice in applied data-analysis tasks using SAS or R statistical software.
    Prerequisite: Graduate standing in Statistics, or instructor permission.



    Credits: 4
  

  • STAT 6130 - Applied Multivariate Statistics

    This course develops fundamental methodology to the analysis of multivariate data. Topics include the multivariate normal distributions, multivariate regression, multivariate analysis of variance (MANOVA), principal components analysis, factor analysis, and discriminant analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied dataanalysis tasks using SAS or R statistical software.
    Prerequisite: Graduate standing in Statistics, or instructor permission.



    Credits: 4
  

  • STAT 6160 - Experimental Design

    This course develops fundamental concepts and methodology in the design and analysis of experiments. Topics include analysis of variance, multiple comparison tests, randomized block designs, Latin square and related designs, factorial designs, split-plot and related designs, and analysis of covariance. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.
    Prerequisite: Graduate standing in Statistics, or instructor permission.



    Credits: 4
  

  • STAT 6250 - Longitudinal Data Analysis

    This course develops fundamental methodology to the analysis of categorical data. Topics include contingency tables, generalized linear models, logistic regression, and logit and loglinear models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.
    Prerequisite: Graduate standing in Statistics, or instructor permission.



    Credits: 4
  

  • STAT 6440 - Introduction to Bayesian Methods

    Course provides an introduction to Bayesian methods with an emphasis on modeling and applications. Topics include the elicitation of prior distributions, deriving posterior and predictive distributions and their moments, Bayesian linear and generalized linear regression, and Bayesian hierarchical models. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.
    Prerequisite: Graduate standing in Statistics, or instructor permission.



    Credits: 4
  

  • STAT 6510 - Advanced Data Experience

    This course develops skills in using data analysis to contribute to research. Each student completes a data-analysis
    project using data from an interdisciplinary research effort. Topics will vary, and are tailored to the objectives of the projects, and may include discussion of computationally intensive statistical methods that are commonly applied in research.



    Credits: 1
  

  • STAT 6520 - Statistical literature

    This course develops skills in reading the statistical research literature and prepares the student for contributing to it. Each student completes a well written and properly formatted paper that would be suitable for publication. The paper reviews literature relevant to a specialized research area, and possibly suggests an original research problem. Topics will vary from term to term.



    Credits: 1
  

  • STAT 7110 - Introduction to the Foundations of Statistics

    This course introduces fundamental concepts in the classical theory of statistical inference. Topics include sufficiency and related statistical principles, elementary decision theory, point estimation, hypothesis testing, likelihood-ratio tests, interval estimation, large-sample analysis, and elementary modeling applications.
    Prerequisite: Graduate standing in Statistics, 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

    Course develops fundamental data-analysis methodology based on generalized linear models.Topics include the origins of generalized linear models, binary and polytomous data, probit analysis, logit models for proportions, log-linear models for counts, inverse polynomial models, quasi-likelihood models, & survival data models. Conceptual disc. is supplemented w/hands-on practice in applied data-analysis tasks using SAS or R statistical software.
    Prerequisite: Graduate standing in Statistics, or instructor permission.



    Credits: 4
  

  • 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

    This course develops fundamental methodology related to the main designs and estimation techniques used in sample surveys. Topics include simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation, and non-response and other non-sampling errors. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software.
    Prerequisite: Graduate standing in Statistics, or instructor permission.



    Credits: 4
  

  • 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 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 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 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: 1 to 9
  

  • STAT 9998 - Non-Topical Research, Preparation for Doctoral Research

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



    Credits: 1 to 12
  

  • STAT 9999 - Non-Topical Research

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



    Credits: 1 to 12

Studio Art
  

  • ARTS 5900 - Graduate Projects in Studio Art

    Advanced problems and situations in art-making including the development of skills related to the creation of new research.



    Credits: 1-3

Systems and 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 2010, APMA 3100 or 3110.



    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 6001 or instructor permission.



    Credits: 3
  

  • SYS 6003 - Optimization I

    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 I

    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 3100, 3120, 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 6620. Prerequisite: APMA 2130 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 5130. Prerequisites: SYS 6018, SYS 4021/6021, or STAT 5120 (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 6003, 6005, or equivalent.



    Credits: 3
  

  • SYS 6016 - Machine Learning

    A graduate-level course on machine learning techniques and applications with emphasis on their application to systems engineering. Topics include: Bayesian learning, evolutionary algorithms, instance-based learning, reinforcement learning, and neural networks. Students are required to have sufficient computational background to complete several substantive programming assignments. Prerequisite: A course covering statistical techniques such as regression. Co-Listed with CS 6316.



    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 6021, SYS 4021, or STAT 5120.



    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 6044 - Engineering Economic Systems

    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 2010, APMA 3100 or 3110.



    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 3100, SYS 3021, 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 6003 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 Processes

    This course covers the design, operation, & maintenance of sustainable water and sanitation infrastructure as integrated municipal systems. It reviews mass & energy balances & unit operations as bases for the processes for water and sanitation (wasan) system design & management. It covers wasan regulation, and introduces the topic of small infrastructure. It also covers the challenges of deteriorating infrastructure, population, & climate change. Prerequisite: Graduate Standing in SEAS or Approval of Instructor



    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 6001, 6003, and 6005.



    Credits: 3
  

  • SYS 7005 - Stochastic Systems II

    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 6005 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 7716. Prerequisite: SYS 6016 or CS 6316.



    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 6001, 6003, 6005, 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 4021 or SYS 6018) 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 6005 or equivalent, SYS 4021 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 6005, 6034, or equivalent.



    Credits: 3
 

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