This course is designed to equip engineers with the knowledge and practical understanding necessary to identify, evaluate, and propose AI solutions for complex problems within engineering systems. Participants will explore fundamental AI concepts, delve into how AI systems function, and learn methods for assessing their reliability and robustness. The course will emphasize real-world applications across diverse industrial environments, fostering an ability to recognize opportunities for AI-enabled innovation and understand the practical considerations for their implementation.

Engineers and technical professionals in various industries who seek to understand and apply AI solutions to real-world engineering challenges.

• Explain and describe the basic principles, techniques, and underlying mechanisms of various artificial intelligence algorithms relevant to modern engineering applications.
• Evaluate and Utilize basic tools and methodologies for assessing the reliability, robustness, and overall performance of AI systems in engineering contexts
• Propose AI-based solutions for engineering applications, considering practical implementation challenges and responsible AI implications.
• Identify and discuss industrial applications of AI-enabled systems in fields such as transportation, manufacturing, and healthcare

Module 1: Overview of AI in engineers: overview, history, and AI classifications, potential and pitfalls

Module 2: Core concepts of machine learning

Module 3: Core concepts of deep learning and generative AI

Module 4: Core concepts in optimization and simulation

Module 5: Evaluating AI system performance: accuracy, reliability, and robustness

Module 6: AI deployment, auditing, and lifecycles

Module 7: AI applications in manufacturing, logistics, and transportation

Module 8: AI applications in healthcare systems (radiology and medical decision-making), infrastructure management

Laura Albert, Ph.D.

Laura Albert, Ph.D. is a Professor of Industrial & Systems Engineering at the University of Wisconsin–Madison and a leading expert in operations research and data analytics. Her work has advanced homeland security, public safety, and election resilience, and her research on aviation security provided the foundation for TSA PreCheck, an achievement recognized with the INFORMS Impact Prize. She is a Fellow of AAAS, INFORMS, and IISE, and the recipient of major honors including a Fulbright Award and NSF CAREER Award.

As Department Chair from 2021-24, she guided UW-Madison's Industrial & Systems Engineering program to new heights in research, rankings, and student success. A Fellow of AAAS, INFORMS, and IISE, she is widely honored for her research and teaching. Known for making complex topics accessible and engaging, Prof. Albert has delivered dozens of keynote talks, written popular blogs, and frequently appeared in national media. She brings that same energy and clarity to the classroom, helping learners see how AI and data analytics can transform engineering and society.