Artificial Intelligence Management Concentration
Carnegie Mellon University is a pioneer and a hub for artificial intelligence. At Heinz College, we have embedded our AI expertise into your master’s education, regardless of whether you are studying information security, public policy, the arts or health care.
At Carnegie Mellon, we view AI as intertwined with everyday life. In entertainment, health care and cybersecurity, in Silicon Valley and on Capitol Hill, change makers are using AI to address the problems that have stymied our efforts to solve them.
To maximize the impact AI can have while minimizing its risk of harm, the world needs leaders who understand the entire AI life cycle and steer the technology toward the greater good. This is where Heinz College lives, and we are proud to offer a concentration in AI Management.
This concentration will be a differentiator in the job market and in your career.
The purpose of this concentration, which is open to current and incoming students in our MSPPM two-year and Data Analytics tracks, as well as our MAM, MSHCA and MSISPM programs, is to build aptitude in operationalizing and governing AI systems. You will complete a semester-long AI Capstone project, during which you’ll work with a real-world client, to reinforce your knowledge and build relevant experience.
As a Heinz master's student, you can opt into the AI Management concentration simply by taking relevant elective coursework at Heinz College or across CMU. Students opting to pursue an AI Management concentration must submit the Concentration Declaration Form and may request a letter of concentration completion from the Heinz College Office of Academic Services.
Coursework Overview
The AI Management concentration coursework includes 18 units of foundational courses and 18 units out of approved electives for a total of 36 units. In addition, students will be encouraged to do a capstone project related to AI to solve a real problem.
Required Foundational Courses (12 - 18 Units):
- a. Demystifying AI (94-703)
Instructor: David Steier
Units: 6
This course is tailored for students in Information Systems and Public Policy Management, focusing on the technical underpinnings of Artificial Intelligence for those without a background in programming. This course provides a deep dive into the core principles of AI, including machine learning algorithms, data analysis, and the mechanics behind technologies such as computer vision and large language models. Through engaging, no-code platforms, students will gain hands-on experience with the tools and techniques driving today's AI innovations. By dissecting real-world applications and their underlying algorithms, this approach ensures that students can critically evaluate and employ AI technologies within their disciplines, laying the groundwork for future exploration and responsible application of AI in societal and policy contexts.
- Or –
- b. Introduction to Artificial Intelligence (95-891)
Instructor: David Steier
Units: 12
Driven by the combination of increased access to data, computational power, and improved sensors and algorithms, artificial intelligence technologies are entering the mainstream of technological innovation. These technologies include search, machine learning, natural language processing, robotics and computer vision. The course begins by describing what the latest generation of artificial intelligence techniques can actually do. After an introduction of some basic concepts and techniques, the course illustrates both the potential and current limitations of these techniques with examples from a variety of applications. We spend some time on understanding the strengths and weaknesses of human decision-making and learning, specifically in combination with AI systems. Exercises will include hands-on application of basic AI techniques as well as selection of appropriate technologies for a given problem and anticipation of design implications. In a final project, groups of students will participate in the creation of an AI-based application. - Fundamentals of Operationalizing AI: Mastering AI Lifecycle from Theory to Practice (94-879)
Instructor: Anand Rao
Units: 6
Understand the AI lifecycle from business scoping, procurement, data management, and engineering to model development, deployment and stewardship. The course introduces the concept of Operationalizing AI, a critical aspect of AI implementation. It will cover its significance, the challenges it presents, its benefits, and the roles involved, focusing on the emerging best practices, roles, skills, capabilities, and governance across the AI lifecycle. - Responsible AI: Principles, Policies, Practices (94-885)
Instructor: Anand Rao
Units: 6
Understand the risks and harms traditional and generative AI can pose, the principles guiding ethical use of AI, and the intricacies of how these harms manifest themselves in the AI lifecycle. This course places a strong emphasis on bias, fairness, transparency, explainability, safety, security, privacy, and accountability, demystifying these foundational concepts and highlighting their relevance in the end-to-end AI life cycle.
Approved Electives (Select 12 - 18 units from the list below):
Course # | Units | Course title |
6 | Generative AI: Applications, Implications and Governance | |
6 | Applications of NL(X) and LLM | |
6 | Generative AI Lab | |
6 | Disruptive Technologies in Arts Enterprises | |
6 | AI Security | |
6 | Managing Analytics Projects | |
12 | ||
12 | Machine Learning for Problem Solving | |
6 | Agent-Based Modeling and Digital Twins | |
6 | Tech Strategy | |
6 | Making Products Count: Data Science for Product Managers | |
6 | Systems Thinking & Discrete Event Simulation | |
6 | Advanced AI and Business Strategy | |
6 | Design Thinking | |
6 | Critical AI Studies for Public Policy |