Artificial Intelligence (Minor)
Bowers College of Computing and Information Science
Program Description
The Cornell Ann S. Bowers College of Computing and Information Science Artificial Intelligence (AI) minor is open to all undergraduates and is designed to provide students with a solid foundational understanding of the algorithms and techniques that underlie AI capabilities.
Academic Standards
Grade Requirements
All qualifying courses must be taken at Cornell for a letter grade. Grades of S/U or SX/UX will not be accepted. Course substitutions or external coursework are also not allowed.
Each course must be completed with a grade of C or better to count toward the minor. Grades of C- will not be accepted.
Minor Declaration Information
Complete the AI Minor application once you are enrolled in or have completed the final courses that you need for the minor.
Questions about the AI minor should be directed to cis.ai-minor@cornell.edu.
Submission Deadlines
If graduating in May or August, the form is due by May 31. If graduating in December, the form is due by December 31. Late submissions will not be accepted.
Program Information
- Minimum Credits for Minor: 15
Minor Requirements
Please note that this is a highly technical minor. The majority of the required and elective courses have mandatory prerequisites that include computer programming, probability, calculus, and/or linear algebra. Please review each course's prerequisites starting with the Foundations courses, and plan your schedule accordingly.
Six courses are required in total:
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There are four required Foundations of AI core courses.
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Two are technical classes – on computational AI methods for learning and reasoning, respectively. These are complemented by a course on the design and evaluation of human-AI systems and a course on AI ethics, governance, and policy.
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Students also select two AI elective courses from the course list provided.
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Students may count a maximum of two courses toward both the AI minor and their own major’s requirements, though they may count other courses they take for the AI minor toward their major’s elective requirements, provided their department approves.
Important Information about Major/Minor Overlaps
Computer Science (CS) Majors
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You may count a maximum of two courses toward both the AI Minor and your CS core courses, CS electives, and/or CS practicum requirements for the CS major.
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You may, however, count other courses you take for the AI Minor toward your CS technical electives, external specialization, major-approved and/or advisor-approved elective coursework, but only if those courses meet the requirements for that category of elective.
Information Science (IS) Majors
The below notes are for students in both the College of Arts & Sciences and College of Agriculture and Life Sciences.
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You may count a maximum of two courses toward both the AI Minor and your IS core courses and/or IS concentration requirements for the IS major.
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You may, however, count other courses you take for the AI Minor toward your IS electives, but only if those courses meet the requirements for that category of elective.
Information Science, Systems, and Technology (ISST) Majors
The below notes are for students in the College of Engineering.
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You may count a maximum of two courses toward both the AI Minor and your ISST core courses and/or primary ISST concentration requirements for the ISST major.
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You may, however, count other courses you take for the AI Minor toward your secondary ISST concentration, advisor-approved electives and major-approved electives but only if those courses meet the requirements for that category of elective
Required Courses
| Code | Title | Hours |
|---|---|---|
| Core Courses | ||
| Foundations of AI: Machine Learning | ||
| Select one of the following: | ||
| CS 3780 | Introduction to Machine Learning (formerly CS 4780) | 4 |
| ECE 3200 | Foundations Machine Learning | 4 |
| ORIE 3741 | Learning with Big Messy Data (formerly ORIE 4741) | 4 |
| STSCI 3740 | Data Mining and Machine Learning (formerly STSCI 4740) | 4 |
| Foundations of AI: Reasoning | ||
| CS 3700 | Foundations of AI Reasoning and Decision-Making (formerly CS 4700) | 3 |
| Foundations of AI: Human-AI Interaction 1 | ||
| INFO 3160 | AI-Assisted Programming Design | 3 |
| INFO 4240 | Designing Technology for Social Impact | 4 |
| or STS 4240 | Designing Technology for Social Impact | |
| INFO 4470 | ||
| INFO 4940 | Special Topics in Information Science (Designing AI Products and Services or Human-AI Interaction Design) 1 | 1-4 |
Designing AI Products and Services | ||
| Foundations of AI: Ethics, Governance & Policy | ||
| Select one of the following: | 3 | |
| ENGRG 3605 | Ethics of Computing and Artificial Intelligence Technologies 2 | 3 |
| or PHIL 2473 | Ethics of Computing and Artificial Intelligence Technologies | |
| or STS 3605 | Ethics of Computing and Artificial Intelligence Technologies | |
| INFO 1210 | Political Economy of Technology | 3 |
| INFO 1260 | Choices and Consequences in Computing 2 | 3 |
| or CS 1340 | Choices and Consequences in Computing | |
| INFO 4210 | Artificial Intelligence: Law, Ethics, and Policy | 3 |
| or PUBPOL 4210 | Artificial Intelligence: Law, Ethics, and Policy | |
| Electives | ||
| Select two of the following: | ||
| BIONB 3500 | NeuroAI: Bridging Brains and AI | 3 |
| BME 4790 | Modern Applications of Machine Learning and Artificial Intelligence for Biomedical Applications | 3 |
| CS 4670 | Introduction to Computer Vision | 4 |
| CS 4701 | Practicum in Artificial Intelligence | 2 |
| CS 4740 | Natural Language Processing | 4 |
| or COGST 4740 | Natural Language Processing | |
| or LING 4474 | Natural Language Processing | |
| CS 4750 | Foundations of Robotics | 4 |
| or ECE 4770 | Foundations of Robotics | |
| or MAE 4760 | Foundations of Robotics | |
| CS 4756 | Robot Learning | 4 |
| CS 4782 | Introduction to Deep Learning | 4 |
| CS 4783 | Mathematical Foundations of Machine Learning | 4 |
| CS 4787 | Principles of Large-Scale Machine Learning Systems | 4 |
| CS 4789 | Introduction to Reinforcement Learning | 3 |
| CS 4860 | Applied Logic | 3 |
| or MATH 4860 | Applied Logic | |
| ECE 4160 | Fast Robots | 4 |
| ENGRG 3605 | Ethics of Computing and Artificial Intelligence Technologies 2 | 3 |
| or PHIL 2473 | Ethics of Computing and Artificial Intelligence Technologies | |
| or STS 3605 | Ethics of Computing and Artificial Intelligence Technologies | |
| INFO 1170 | AI in Organizations | 3 |
| INFO 1260 | Choices and Consequences in Computing 2 | 3 |
| or CS 1340 | Choices and Consequences in Computing | |
| INFO 3160 | AI-Assisted Programming Design 3 | 3 |
| INFO 3350 | Text Mining History and Literature | 3 |
| INFO 3950 | Advanced Data Analytics | 3 |
| INFO 4120 | Ubiquitous Computing | 3 |
| INFO 4300 | Language and Information | 3 |
| INFO 4240 | Designing Technology for Social Impact 3 | 4 |
| or STS 4240 | Designing Technology for Social Impact | |
| INFO 4260 | Computing On Earth: Planetary Dimensions and Consequence of Computing | 3 |
| or STS 4260 | Computing On Earth: Planetary Dimensions and Consequence of Computing | |
| INFO 4300 | Language and Information | 3 |
| or CS 4300 | Language and Information | |
| INFO 4310 | Interactive Information Visualization | 3 |
| INFO 4410 | Re-Designing Robots | 3 |
| INFO 4470 | 3 | |
| INFO 4940 | Special Topics in Information Science | 1-4 |
Advanced NLP for Humanities Research | ||
Applied Machine Learning: Methods and Applications | ||
Designing AI Products and Services 3 | ||
Law, Policy, and Politics of AI | ||
Neural Networks in Practice | ||
| ILRGL 4066 | Technological Change at Work | 3 |
| or AMST 4066 | Technological Change at Work | |
| LING 4424 | Computational Linguistics I | 4 |
| or COGST 4240 | Computational Linguistics I | |
| or CS 4744 | Computational Linguistics I | |
| LING 4434 | Computational Linguistics II - Interpreting Language Models | 4 |
| or CS 4745 | Computational Linguistics II - Interpreting Language Models | |
| MAE 4180 | Autonomous Mobile Robots | 3 |
| or CS 4758 | Autonomous Mobile Robots | |
| or ECE 4180 | Autonomous Mobile Robots | |
| MAE 4810 | Robot Perception | 3 |
| or ECE 4240 | Robot Perception | |
| NBA 4920 | AI for Business Applications | 1.5-3 |
| ORIE 3320 | Optimization for AI | 4 |
| ORIE 4160 | Topics in Data Science and OR | 3 |
| ORIE 4740 | Statistical Data Mining I | 4 |
| ORIE 4742 | Info Theory, Probabilistic Modeling, and Deep Learning with Scientific and Financial Apps | 3 |
| ORIE 4570 | Reinforcement Learning with Operations Research Applications | 3 |
| PHIL 2621 | Minds and Machines | 3 |
| or COGST 2621 | Minds and Machines | |
| PUBPOL 2120 | Disruptive and Emerging Technologies: Policy and Practice | 3 |
| PUBPOL 3520 | Economic and Policy Implications of Artificial Intelligence | 3 |
| PUBPOL 4210 | Artificial Intelligence: Law, Ethics, and Policy 2 | 3 |
| or INFO 4210 | Artificial Intelligence: Law, Ethics, and Policy | |
| STSCI 4030 | Linear Models with Matrices | 4 |
| STSCI 4520 | Statistical Computing | 4 |
| STSCI 4750 | Understanding Machine Learning | 4 |
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Note: Students graduating in Dec 2026 or May 2027 may use INFO 3450 Human-Computer Interaction Design as an alternative.
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Cannot be used jointly to fulfill the Foundations of AI: Ethics, Governance & Policy.
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Cannot be used jointly to fulfill the Foundations of AI: Human-AI Interaction.