Level 1: AI-Exposure Requirement
Complete the First Engineering Requirement by following one of the four pathways:
- ENGR 13100 and ENGR 13200
- ENGR 13000
- ENGR 13300 + VIP/EPICS
- ENGR 16100 and ENGR 16200
Topics covered: Knowledge about how AI works; Critical thinking; Assess and evaluate AI responses; Data-informed decision making; Ethical aspects of AI.
Level 2: AI-Knowledge Requirement
All MSE students are required to take the following two classes in MSE which include the essential elements of AI in the context of MSE:
- MSE 230 Structure and Properties of Materials Engineering
- MSE 235 2nd Year Materials Engineering Laboratory
Several AI modules in these two courses will be based on the hands-on learning modules in https://nanohub.org/groups/mlmodules
Level 3: AI-Subject Requirements
All MSE students should take at least two of the following courses that either use AI in subject or strengthen the probabilistic thinking aspect of AI:
- MSE 335 3rd Year Materials Engineering Laboratory
- MSE 370 Electrical, Optical and Magnetic Properties of Materials
- MSE 382 Mechanical Response of Materials
- MSE 497 Python for Materials Data Science
- MSE 570 Introduction to Materials Modeling and Informatics
- STAT 31100 - Introductory Probability
- STAT 35000 - Introduction To Statistics
- STAT 51100 - Statistical Methods
- STAT 51200 - Applied Regression Analysis
- STAT 51300 - Statistical Quality Control
- STAT 51400 - Design Of Experiments
- STAT 51600 - Basic Probability And Applications
- IE 33000 - Probability And Statistics In Engineering II
Level 4: AI-Builder Requirement
Senior Design: As part of classroom lectures delivered to senior design students who are working on a wide variety of industrial projects, MSE will cover the following essential topics over 2-3 total lectures: active learning for design of experiments (requires data, regression, and decision -making skills), data security, IP, and NDAs, and efficiently and ethically using AI to assist with writing code, collecting information and ideas, and for writing reports.
Example projects in senior design include generative AI models to predict microstructures. Students will be asked to discuss how AI has been / will be used: AI is being used to validate microstructure created by a previously trained generative model, and to come with solutions for how to improve the model going forward. AI is utilized for writing code and performing image analysis. Students are also learning about data security and IP.