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.

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. Prerequisite: As specified for each offering.

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).

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 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.

"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)."

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.

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.

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.