MSE 5750

Artificial Intelligence: Concepts and Applications

Course Description

Course information provided by the 2025-2026 Catalog.

Course provides a general overview of the Artificial Intelligence (AI) concepts and techniques most relevant to materials science and engineering. Starting with a brief introduction to the history of AI in the 20th century and its many definitions, students will be introduced to some of the most commonly used techniques, including (but not limited to): neural nets, supervised and unsupervised learning, Gaussian processes, decision trees and Large Language Models (LLMs). Understanding will be developed by considering a series of case studies from the materials science literature, which showcase applications of these techniques. The course will also cover explainable AI (xAI) and its role in scientific discovery, and ethical issues related to the use of some AI techniques. Students requiring in-depth, highly technical explanations of AI techniques are directed to the relevant courses in the Computer Science department.

Last 4 Terms Offered (None)

Learning Outcomes

• Demonstrate proficiency in the main mathematical methods (discrete mathematics and linear algebra) underlying AI techniques at a level appropriate for this class.
• Apply knowledge of AI techniques to selecting the most appropriate approaches to materials problems.
• Identify and discuss ethical issues and societal implications related to the use of some AI techniques.
• Synthesize knowledge of AI techniques and materials applications by presenting a recent “AI in materials” use case from the literature.

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