Today AI is driving significant innovation across products, services, and systems in every industry, and tomorrow’s AI engineers will have the advantage.
Students will learn how to design and build AI-orchestrated systems capable of operating within engineering constraints. At Carnegie Mellon we are leading this transformation by teaching students how to simultaneously design a system’s functionality and supporting AI mechanisms, including both its AI algorithms and the platform on which the AI runs, to produce systems that are more adaptable, resilient, and trustworthy.
Students pursuing the M.S. in Artificial Intelligence Engineering - Materials Science and Engineering will be able to:
- Apply deeper knowledge of AI methods, systems, tool chains, and cross-cutting issues including security, privacy, and other ethical and societal challenges
- Identify the engineering constraints that AI-orchestrated systems must operate within
- Solve practical problems using AI methods
- Adapt to the latest AI-enabled tools and learn how to work with and “advise” machines
- Demonstrate graduate-level domain knowledge in materials science
Requirements
Students with a bachelor's degree in materials science and engineering or a related discipline with an interest in the intersection of AI and engineering are encouraged to apply to this program.
Students should be able to demonstrate proficiency in:
- Programming (Python preferred) for data analysis
- Probability/statistics such as probability, distributions, joint and conditional probability, independence, marginalization, Bayes rules, and maximum likelihood estimation
- Linear algebra topics such as matrix operations, linear transformations, projections, matrix derivatives, and eigendecomposition
English proficiency is required for non-native English speaking applicants.
Relevant curriculum
The MS AI program is completed in 3 semesters with 120 units of coursework and the completion of a capstone or research project
Core courses
- Systems and Tool Chains for AI Engineering
- Introduction to Machine Learning for Engineers
- Introduction to Deep Learning for Engineers
- Trustworthy AI
- Methods of Computational Materials Science
- 2 of the MSE core graduate courses
Endless opportunities
Whether pursuing academia or industry, this degree uniquely positions students for the future of research and high demand careers with a mastery of integrating engineering domain knowledge into AI solutions.