Extended Reality for Engineering

ISYE/ME 4803/8803

This course covers the fundamentals of the extended reality (XR) development lifecycle, with a special focus on engineering applications. Students will learn to use the Unity game engine to create XR apps compatible with various platforms. The course explores XR’s affordances for human-machine interaction, immersive design visualization, virtual prototyping, training, remote operation, and real-time collaboration. Topics include overview of XR concepts, hardware, software, and industrial use cases; UX/UI design, storyboarding, bodystorming, and conceptual design; C# and object-oriented programming; Unity Editor basics, gameplay mechanics, version control, effects, animations, UI, and prototyping; VR development using XR Interaction Toolkit, covering interaction design, ergonomics, and optimization; and AR development using AR Foundation, exploring mobile and head-mounted AR, marker-based AR, plane detection, and interaction design. A semester-long project requires teams to design, develop, and test an XR apps addressing a real-world engineering problem, emphasizing hands-on experience and team-based problem-solving.
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Methods & Applications of Machine Learning

ISYE 4600

This course introduces the available methods in machine learning, which methods are better in what situations, objective functions and errors, and how to tune parameters. Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. This course is designed to address the most fundamental questions in machine learning: What are the most important methods to know, and why? How can we determine whether one method is better than another for a specific dataset of interest? What can we say about the errors our methods will make on future data? What is the “right” objective function? How should parameters be tuned? This course is designed to give senior undergraduate students a thorough grounding in the methods, theory, mathematics, and algorithms needed to understand and apply machine learning.
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Human-Centered Systems

ISYE 4009

This course introduces foundational principles of studying human cognition, action, and interactions with machines in industrial settings. Topics include general cognitive systems engineering concepts and principles, and specific concepts and principles of interface design, task analysis, prototyping, and empirical usability of evaluation methods. Students learn the fundamentals of human information processing, visual and multisensory perception, attention, memory, problem-solving, decision-making, expertise, response selection, principles of engineering anthropometry and human-centered design. Students also learn to process and statistically analyze data from various human physiological and behavioral sensors using R for in-class exercises and the iMotions software suite in the HUMAN Lab (Human Understanding, Modeling, Analysis, and Neurocognition) at the ADC XR Makerspace, which serves as the companion lab module for the HIS course. Students engage in weekly lab activities using advanced sensors such as EEG headsets, eye-tracking glasses, ECG and GSR sensors, and cameras for facial expression recognition and hand/body tracking to analyze human behavior and cognitive processes. The course emphasizes hands-on activities in both classroom and lab environments, preparing students to design, implement, and evaluate human-integrated systems in industry. 
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