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 SYS 3501.

Focuses on the evaluation of candidate system designs and design performance measures. Includes identification of system goals; requirements and performance measures; design of experiments for performance evaluation; techniques of decision analysis for trade-studies; presentation of system evaluation and analysis results. Illustrates the concepts and processes of systems evaluations using case studies. Pre-reqs: APMA 3120, SYS 2001, & SYS 3021.

Major dimensions of systems engineering will be covered and demonstrated through case studies: (1) The history, philosophy, art, and science upon which systems engineering is grounded; including system thinking and guiding principles and steps in the `systems engineering approach¿ to problem solving; and (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.

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)

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

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: 2nd Year or higher standing in 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.

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.

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.