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 …
This course demystifies the building blocks and computational methods necessary to design and evaluate AI-driven systems. Students will gain hands-on experience in R and Python and leverage generative AI responsibly …
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 …
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 …
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 …
A first course in the theory & practice of discrete-event simulation. Monte Carlo methods, generating random numbers & variates, spreadsheet add-ins & applications, sampling distributions & confidence intervals, input analysis …
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 …
"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 …
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 …
A design project extending throughout the fall and spring semesters. Involves the study of a real-world, open-ended situation, including problem formulation, data collection, analysis and interpretation, model building and analysis, …
A design project extending throughout the fall and spring semesters. Involves the study of a real-world, open-ended situation, including problem formulation, data collection, analysis and interpretation, model building and 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. …
Detailed study of a selected topic determined by the current interest of faculty and students. Offered as required. Prerequisite: As specified for each offering.
Detailed study of a selected topic determined by the current interest of faculty and students. 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, …
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 …
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. …
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 …