M.S. in Applied AI for Food and Nutrition
The School of Engineering and the Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy jointly offer the Master of Science in Applied AI for Food and Nutrition, a 30-credit online degree designed to be completed on a part-time schedule. The program brings together artificial intelligence and nutrition science, giving students the knowledge to understand how AI works and apply it thoughtfully to challenges in food, nutrition, and health.
No advanced technical background is required. You’ll learn tools such as Python as part of the program, with resources available to help you prepare if coding is new to you. The program is designed with working professionals in mind. Prerequisites include one undergraduate-level course in general biology (preferred), general chemistry, or general physiology with a grade of B- or higher (no lab required). You may apply for admission while still completing this requirement, but it must be completed by the time you matriculate.
The degree requires a minimum of 30 credits and the fulfillment of at least 10 courses; all courses must be at the 100 level or above. Students complete core coursework in artificial intelligence and nutrition, select electives based on their interests, and complete an applied experiential learning requirement.
Degree Requirements
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All students complete four core AI courses offered through the School of Engineering. Together, these courses provide a technical foundation in how AI systems work, how they are built and evaluated, and how they can be used responsibly.
- AI 201 – Understanding AI: Systems, Tools, and Applications
- AI 202 – Working with AI: Data, Tools, and Pipelines
- AI 203 – Evaluating AI: Risk, Uncertainty, and Failure
- AI 204 – AI Ethics, Policy, and Responsible Use
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Nutrition Science
Students complete a foundation in nutrition science through the Friedman School.- NUTC 202 – Principles of Nutrition Science
or, with guidance from an academic advisor, students complete a two-course sequence instead of NUTC 202:
- NUTR 245 – Scientific Basis of Nutrition: Micronutrients
- NUTR 246 – Scientific Basis of Nutrition: Macronutrients
AI and Data Analytics in Nutrition
Students also complete courses designed to connect the program's AI foundation directly with nutrition research and applications.
- NUTR-ON 390 – Introduction to AI in Nutrition
- NUTR-ON 393 – Data Visualization
- NUTR-ON 394 – Advanced Data Analysis
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Depending on the nutrition science sequence selected, students complete one or two elective courses for a total of 3–6 elective credits. Electives allow students to develop additional expertise in areas such as biochemical and molecular nutrition, climate and food systems, nutritional epidemiology, precision nutrition, and nutrition entrepreneurship and innovation.
Sample electives include:
- NUTR 248 – Precision Nutrition
- NUTR-ON 288 – Customer Discovery and Innovation: "Foraging for Market Fit"
- NUTR 301 – Nutrition in the Life Cycle
- NUTR 305 – Nutritional Epidemiology
- NUTR-ON 256 – Climate Change: Risk and Adaptation for Food Systems and Beyond
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All students complete a minimum of 120 hours of applied experiential learning. This non-credit requirement may be fulfilled through internships, project-based work, or collaborations with industry and research organizations. Students apply AI to a real-world challenge in nutrition or food systems, with the experience approved by both a project sponsor and academic advisor.
Students also complete a short reflection connecting the experience to their degree and career goals.