This course examines the lifecycle of engineered systems (ES) and the public policies developed to regulate them. It covers risks, costs, benefits, and equity as common evaluation criteria for ES and their regulatory policies. It uses case studies and basic tools of decision analysis to critically evaluate the tradeoffs involved in developing and regulating ES through public policy. Pre-reqs: (STS 1500 or ENGR 1020 or ENGR 2595 - Engineering Foundations II) and (APMA 1110 or MATH 1320), and (CHEM 1410 or CHEM 1810), and (PHYS 1425 or PHYS 1420 or PHYS 1710).

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)

This course provides students with the background necessary to model, store, manipulate, and exchange information to support decision making. It covers Unified Modeling Language (UML), SQL, and XML; the development of semantic models for describing data and their relationships; effective use of SQL; web-based technologies for disseminating information; and application of these technologies through web-enabled database systems. Corequisite: CS 2100 or CS 2110.

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. Prerequisite: APMA 3100, SYS 3021, or equivalent.

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.

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

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

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

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

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).