Artificial Intelligence Ph.D. Requirements
Complete these requirements for an Artificial Intelligence Ph.D.
Admission Requirements
- Master’s degree, normally in a computing, engineering, or science related field or a Bachelor’s degree, normally in a computing, engineering, or science related field.
- An overall GPA of at least 3.0 (on a 4.0 scale) for the bachelor's degree, and master's if applicable.
- Satisfy the School of Graduate Studies’ English Language Proficiency requirements as listed in the Graduate Academic Information section.
The School of Electrical Engineering and Computer Science recognizes that the prerequisite expertise identified above may be acquired in a variety of ways. Students who do not meet all of the requirements may be admitted with provisional status with the obligation to meet the remaining requirements early in their graduate study.
Degree Requirements
Students seeking the Doctor of Philosophy in Computer Science degree must satisfy all general requirements set forth by the School of Graduate Studies. In addition, they must meet the following requirements set by the School of Electrical Engineering and Computer Science:
- Completion of 90 credit hours beyond the bachelor's degree.
- Maintain a GPA of at least 3.0 for all classes completed as a graduate student.
Requirements for Students with an Approved Master's Degree
Master's degree must be in an approved topic related to computing.
- Complete 15 credit hours from the list of Core Required Courses
- Complete 6 credit hours in each of 3 groups of elective courses (18 credit hours total)
- Complete at least 6 credit hours of EECS 510 Artificial Intelligence Seminar
- Complete 21 credit hours of Dissertation Research
- Successfully complete the Qualifying Examination consisting of both the written proposal and an oral defense.
- Oral Final Examination which includes a defense of the student’s dissertation. The oral defense of the student’s dissertation must take place at least one semester after satisfactory completion of the comprehensive examination.
- Submission of the dissertation document, approved by the student’s Faculty Advisory Committee.
Requirements for Students with an Approved Bachelor Degree
- Complete 9 credit hours of Background Courses
- Complete 15 credit hours from the list of Core Required Courses
- Complete 9 credit hours in each of 3 groups of elective courses (27 credit hours total)
- Complete at least 9 credit hours of EECS 510 Artificial Intelligence Seminar
- Complete 30 credit hours of Dissertation Research
- Successfully complete the Qualifying Examination consisting of both the written proposal and an oral defense.
- Oral Final Examination which includes a defense of the student’s dissertation. The oral defense of the student’s dissertation must take place at least one semester after satisfactory completion of the comprehensive examination.
- Submission of the dissertation document, approved by the student’s Faculty Advisory Committee.
Background Courses
Course List| Code | Title | Credits |
|---|
| DATA 511 | | 3 |
| DATA 512 | | 3 |
| DATA 513 | | 3 |
Core Required Courses
Course List| Code | Title | Credits |
|---|
| DATA 530 | Artificial Intelligence | 3 |
| DATA 532 | Applied Machine Learning | 3 |
| PHIL 570 | Philosophical and Ethical Implications of AI and Emerging Technologies | 3 |
| DATA 541 | | |
| CSCI 548 | | |
Elective Group 1: AI Foundations
Course List| Code | Title | Credits |
|---|
| CSCI 575 | Analysis of Algorithms | 3 |
| PSYC 532 | Cognitive and Behavioral Foundations in AI | 3 |
| POLS 504 | | |
| COMM 406 | Future of Communication Technology | 3 |
| SOC 460 | Technology and Society (Technology and Society) | 3 |
| HIST 530 | History of Technology (History of Technology) | 3 |
| PSYC 539 | Cognitive Psychology | 3 |
Elective Group 2: Advanced AI Techniques
Course List| Code | Title | Credits |
|---|
| CSCI 543 | Machine Learning | 3 |
| CSCI 544 | Soft Computing: Computational Intelligence I | 3 |
| CSCI 554 | Applications in AI/Computational Intelligence | 3 |
| DATA 525 | Data Engineering and Mining | 3 |
| DATA 527 | Predictive Modeling | 3 |
| DATA 540 | Data Visualization | 3 |
| CSCI 542 | | |
| CSCI 549 | | |
Elective Group 3: Machine Vision and Robotics
Course List| Code | Title | Credits |
|---|
| EE 751 | Wireless Sensor Networks | 3 |
| EE 752 | Introduction to Autonomous Systems | 3 |
| ME 580 | Introduction to Autonomous Robotics | 3 |
| ME 566 | Introduction to Machine Vision | 3 |
| EE 563 | Digital Image Processing | 3 |
| EE 557 | Robotics Fundamentals | 3 |
Elective Group 4: AI Applications
Course List| Code | Title | Credits |
|---|
| BIMD 514 | Foundations of Bioinformatics | 3 |
| CHEM 534 | Quantum and Computational Chemistry | 3 |
| COMM 522 | Data Mining Analytics for Communication Professionals | 3 |
| EFR 530 | Learning Analytics | 3 |
| COMM 549 | Information Communication Technologies | 3 |
| EFR 535 | Data Analytics and Visualization with R | 3 |
| PSYC 533 | Theories of Learning | 3 |
| PSYC 540 | Foundations of Behavioral Data Analytics | 3 |
| PHIL 575 | Data Science Ethics | 3 |
| PSYC 537 | Physiology of Behavior and Psychophysiological Measurement | 3 |
Graduate Admissions Requirements
In addition to meeting these program requirements, you must also meet the UND School
of Graduate Studies admissions requirements to apply for this program.
Graduate Requirements
The requirements on this page are pulled from UND’s academic catalog and may not reflect
future terms. Updates are published annually in April.