PhD in Artificial Intelligence
The Doctor of Philosophy in Artificial Intelligence (PhD AI) program offers an advanced, research-focused education in the transformative field of artificial intelligence. AI is revolutionizing industries such as healthcare, technology, and manufacturing, with applications in areas like machine learning, generative AI, natural language processing, and autonomous systems. This program equips students with expertise in core and advanced topics, including deep learning, computer vision, and bioinformatics, while fostering interdisciplinary collaboration and innovation. Students engage in rigorous research under the mentorship of renowned faculty, contributing to groundbreaking advancements that address real-world challenges. Graduates are prepared for high-impact careers as AI scientists, academic researchers, and industry leaders, or for entrepreneurial ventures in cutting-edge AI-driven technologies. With the global AI market expanding rapidly, this program offers unmatched opportunities for professional growth and societal impact.
Degree Requirements
The PhD AI program requires a minimum of 72 credit hours beyond the bachelor’s degree. All students completing the PhD must fulfill the following minimum requirements:
- Complete the graduate coursework with a grade point average (GPA) of B (3.0) or better.
- Complete 15 hours of 8000-level coursework from the list of courses (foundational or elective) in the program curriculum, exclusive of research, problems, and independent study experiences.
- Complete 24 hours of 7000- and 8000-level coursework from the list of courses (foundational or elective) in the program curriculum, exclusive of research, problems, and independent study experiences.
- The doctoral committee may recommend that up to 30 hours of post-baccalaureate graduate credit from other programs at MU or a regionally accredited University be transferred toward the total hours required for the doctoral degree. Transfer credits cannot be used toward the 8000-level requirement.
- Pass a qualifying process within two years of program enrollment and pass a comprehensive examination within five years of program enrollment.
- Complete a doctoral dissertation on a topic approved by the doctoral committee. Defend the dissertation in a final oral examination. A doctoral dissertation for satisfying the degree requirement of the PhD in Computer Science (PhD CS) or the PhD in Electrical and Computer Engineering (PhD ECE) cannot be used for satisfying the degree requirement of PhD AI.
| Foundation Courses | ||
| CMP_SC 7720 | Introduction to Machine Learning and Pattern Recognition | 3 |
| or ECE 7720 | Introduction to Machine Learning and Pattern Recognition | |
| CMP_SC 7750 | Artificial Intelligence I | 3 |
| or ECE 7750 | Artificial Intelligence I | |
| CMP_SC 7770 | Introduction to Computational Intelligence | 3 |
| or ECE 7870 | Introduction to Computational Intelligence | |
| CMP_SC 8725 | Supervised Learning | 3 |
| or ECE 8725 | Supervised Learning | |
| CMP_SC 8735 | Unsupervised Learning | 3 |
| or ECE 8735 | Unsupervised Learning | |
| CMP_SC 8750 | Artificial Intelligence II | 3 |
| CMP_SC 8770 | Neural Networks | 3 |
| or ECE 8890 | Neural Networks | |
| CMP_SC 8001 | Advanced Topics in Computer Science (Topic: Deep Learning) | 1-4 |
| or ECE 8001 | Advanced Topics in Electrical and Computer Engineering | |
| Elective Courses | ||
| CMP_SC 7010 | Computational Methods in Bioinformatics | 3 |
| CMP_SC 7540 | Neural Models and Machine Learning | 3 |
| or ECE 7540 | Neural Models and Machine Learning | |
| CMP_SC 7560 | Mathematical Modeling I | 3 |
| or ECE 7655 | Digital Image Processing | |
| CMP_SC 7730 | Building Intelligent Robots | 4 |
| or ECE 7340 | Building Intelligent Robots | |
| CMP_SC 7740 | Interdisciplinary Introduction to Natural Language Processing | 3 |
| CMP_SC 7820 | Introduction to Digital Signal Processing | 3-4 |
| or ECE 7830 | Introduction to Digital Signal Processing | |
| ISE 7565 | Smart Manufacturing Systems | 3 |
| CMP_SC 8085 | Problems in Computer Science (At most 3 credit hours) | 1-3 |
| ECE 8010 | Supervised Study in Electrical Engineering (At most 3 credit hours) | 1-3 |
| CMP_SC 8130 | Computational Genomics | 3 |
| CMP_SC 8150 | Integrative Methods in Bioinformatics | 3 |
| CMP_SC 8170 | Computational Modeling of Molecular Structures | 3 |
| CMP_SC 8180 | Machine Learning Methods for Biomedical Informatics | 3 |
| CMP_SC 8370 | Data Mining and Knowledge Discovery | 3 |
| CMP_SC 8580 | Machine Learning in Neuroscience | 3 |
| or ECE 8580 | Machine Learning in Neuroscience | |
| CMP_SC 8650 | Advanced Image Processing | 3 |
| or ECE 8855 | Advanced Image Processing | |
| CMP_SC 8675 | Biomedical Image Processing | 3 |
| or ECE 8675 | Biomedical Image Processing | |
| CMP_SC 8690 | Computer Vision | 3 |
| or ECE 8690 | Computer Vision | |
| CMP_SC 8740 | Advanced Natural Language Processing | 3 |
| CMP_SC 8790 | Filtering, Tracking and Data Fusion | 3 |
| CMP_SC 8810 | Advanced Digital Signal Processing | 3 |
| or ECE 8810 | Advanced Digital Signal Processing | |
| CMP_SC 8062 | Probability and Stochastic Processes for Engineers | 3 |
| or ECE 8860 | Probability and Stochastic Processes for Engineers | |
| CMP_SC 8870 | Modeling and Management of Uncertainty | 3 |
| or ECE 8870 | Modeling and Management of Uncertainty | |
| MAE 8930 | Advanced Mechanical System Modeling and Optimization | 3 |
| CV_ENG 8106 | Advanced Intelligent Transportation Systems | 3 |
| Research Courses | ||
| CMP_SC 8980 | Research Masters Project in Computer Science | |
| or ECE 8990 | Research-Master Thesis in Electrical and Computer Engineering | |
| CMP_SC 8990 | Research-Masters Thesis Computer Science | |
| or ECE 8990 | Research-Master Thesis in Electrical and Computer Engineering | |
| CMP_SC 9990 | Research-Doctoral Dissertation Computer Science | |