Three major dimensions of systems engineering will be covered, and their efficacy demonstrated through case studies: (1) The history, philosophy, art, and science upon which systems engineering is grounded; including guiding principles and steps in the 'systems engineering approach' to problem solving; (2) The basic tools of systems engineering analysis, including; goal definition and system representation, requirements analysis, system assessment and evaluation, mathematical modeling, and decision analysis; and (3) system and project planning and management.

Introduction to deterministic optimization models: theory, algorithms, and applications. Coverage begins with highly structured network optimization models and ends with unstructured linear optimization models. Applications include (1) telecommunications network planning and design, (2) design and utilization of transportation and distribution networks, and (3) project management and scheduling. Corequisite: SYS 2001 and APMA 3080.

An introduction to the fundamentals for the analysis, design and evaluation of human-centered systems. For example, user interaction can be designed to leverage the strengths of people in controlling automation and analyzing data. Course topics include Task, User and Work Domain Analysis, User Interface Design Principles, Human Cognition and Information Processing (Top-Down Design), Human Perception (Bottom-Up Design), and Usability Testing. Corequisite: SYS 2001.

Focuses on the practice of systems engineering directly from current systems engineers. A variety of topics are covered by invited speakers from industry, government, and the academy. Discussions include engineering design projects, alternative career paths, graduate studies, professional development, and more immediate options with opportunities for summer internships and capstone projects. Prereq: 3rd Year standing in systems engineering.

A third-year level undergraduate course focused on a topic not normally covered in the course offerings. The topic usually reflects new developments in the systems and information engineering field. Offering is based on student and faculty interests. Prerequisites: Instructor Permission

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. Prerequisite: CS 2100, APMA 3100 and APMA 3120.

"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 & hardware etc.) Topics include: congestion pricing in networks, pricing and efficiency in electricity markets, planned obsolescence in software development, ""network"" effects and the dynamics of technology adoption etc. Prerequisites: ECON 2010 and a course in probability (either APMA 3100, APMA 3110, or Math 3100)."

This course is an introduction to the theory, methods, and applications of risk analysis and systems engineering. The topics include research and development priorities, risk-cost-benefit analysis, emergency management, human health and safety, environmental risk, extreme events, infrastructure resilience, system interdependencies, and enterprise systems. Corequisites: a course in probability (APMA 3100 or APMA 3110 or Math 3100).

A design project extending throughout the fall semester. Involves the study of an actual open-ended situation. Includes problem formulation, data collection, analysis and interpretation, model building & analysis, and generation of solutions. Prerequisite: must have successfully completed 6 or more courses in the standard SYS curriculum (SYS 2001, SYS 2202, and 4 of the following: SYS 3021, SYS 3023, SYS 3034, SYS 3060, and SYS 3062).

This is a colloquium that allows fourth-year students to learn about engineering design, innovation, teamwork, technical communication, and project management in the context of their two-semester systems capstone design project. Prerequisite: must have successfully completed 6 or more courses in the standard SYS curriculum (SYS 2001, SYS 2202, and 4 of the following: SYS 3021, SYS 3023, SYS 3034, SYS 3060, and SYS 3062)

Detailed study of a selected topic determined by the current interest of faculty and students. Offered as required. Prerequisite: As specified for each offering.

Independent study or project research under the guidance of a faculty member. Offered as required. Prerequisite: As specified for each offering.

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

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.

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.

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.

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.

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.

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.

This course will teach students the required skills, concepts, and algorithms to develop mobile robots that act autonomously in complex environments. The main emphasis is on mobile robot locomotion and kinematics, control, sensing, localization, mapping, path planning, and motion planning. Besides theory, students are exposed to simulation environments and lab exercises with real robotic systems.

For master's students.

Special Topics in Distance Learning

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

This course is designed to develop cross-competency in the technical, analytical, and professional capabilities necessary for the emerging field of Cyber-Physical Systems (CPS). It provides convergence learning activities based around the applications, technologies, and system designs of CPS as well as exploring the ethical, social, and policy dimensions of CPS work. The course also emphasizes the importance of communication as a necessary skill.

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

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

Bayesian theory of forecasting and decision making; judgmental procedures and statistical models for probabilistic forecasting, post-processors of deterministic forecasts; sufficient comparisons of forecasters, verification of forecasts, combining forecasts; optimal decision models using probabilistic forecasts including static decision models, sequential decision models, stopping-control models; economic value of forecasts. Prerequisite: SYS 6005 or STAT 6190.

Weekly meeting of graduate students and faculty for presentation and discussion of contemporary systems engineering problems and research. This seminar is offered every spring and fall semesters.

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.

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

For doctoral students.

For doctoral students.