Master of Science in AI Systems Management Curriculum
How do you learn a field that innovates at the speed of AI?
At Heinz College, we stress "learning to learn": providing you with the background, systems thinking and first principles you'll need to adapt and advance along with AI.
AI Systems Management Curriculum
For detailed curriculum information, please visit the Master of Science in AI Systems Management student handbook.
- Advanced AI and Business Strategy (94-829)
- Generative AI: Applications, Implications and Governance (94-816)
- Artificial Intelligence for Social Good (17-737)
- Ethics and Artificial Intelligence (45-848)
- Managing Analytics Projects (94-881)
- AI & Emerging Economies (94-894)
- Making Products Count: Data Science for Product Managers (95-851)
- Big Data and Large Scale Computing (95-869)
- A/B Testing, Design, and Analysis (95-819)
- Tech Strategy (94-888)
- Methods of Policy Analysis: Future of Work (90-745)
- Economic Analysis (95-710)
- Applications of NL(X) and LLM (94-812)
- Agile Methods (95-874)
*The course titles, descriptions and course content are subject to revisions as the industry and technology evolves.
Elective availability may differ by pathway/location; students may also enroll in graduate-level courses from other Heinz College programs as well as other departments across Carnegie Mellon University’s campus, with approval.
For class, syllabi, and faculty information, please visit the Heinz College course catalog.
AI Classes at Heinz College
Sample AI Systems Management Class Schedule
- At Carnegie Mellon University, courses are counted in units instead of credits.
- Three units are roughly equivalent to one credit at many of our peer institutions.
- A semester-long course is 12 units, while a half-semester class (called a mini) is six units.
The following schedules are intended to be examples of the structure of the semester. Courses may not always be offered in this fashion.
Begin Your Path to Impact
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