MS in Artificial Intelligence

The Master of Science in Artificial Intelligence (MS AI) program provides advanced education in the rapidly evolving field of AI, equipping students with the skills to address today’s most pressing technological challenges. The program offers a robust curriculum combining foundational knowledge in machine learning, deep learning, and AI with advanced topics like natural language processing, computer vision, and bioinformatics. Students engage in hands-on learning through research projects and interdisciplinary collaborations, fostering innovation and real-world problem-solving. Graduates are well-prepared for high-demand careers as machine learning engineers, data scientists, and AI researchers in industries such as healthcare, finance, and manufacturing. With the global AI market expanding rapidly, graduates also have opportunities for entrepreneurial ventures or further academic pursuits in cutting-edge AI research.

Degree Requirements

Students need to complete at least 30 credit hours of graduate coursework with a grade point average (GPA) of B (3.0) or better as well as the requirements listed below.

  • Complete at least 15 credit hours of 8000-level coursework from the list of courses in the program curriculum.
  • Complete at least 12 credit hours of foundational courses in the program curriculum.
  • A maximum of 6 credit hours may be graduate credits transferred from other MU programs or other institutions. Credit transfer must be recommended by the student’s advisor and approved by the program’s director of graduate studies and the Graduate School. Transfer credits cannot be used toward the 8000-level coursework requirement.
  • A maximum of 15 credit hours used to satisfy the degree requirement of MS in Computer Science (MS CS), MS in Computer Engineering (MS CE), MS in Electrical Engineering (MS EE), PhD in Computer Science (PhD CS), or PhD in Electrical and Computer Engineering (PhD ECE) may be used toward the MS AI degree requirement.

The MS degree has two options: Thesis and Non-Thesis.

  • Thesis option: A student must take 3 to 6 credit hours of CMP_SC 8990 and complete a thesis. A student must complete an independent project under a faculty advisor approved by the EECS department. The thesis project is documented in a formal thesis, presented to a faculty committee of at least three graduate faculty members approved by the EECS department and defended in a public defense in a final oral examination. 
  • Non-Thesis option: A student must take 1 to 3 credit hours of CMP_SC 8980 and complete a substantial independent project under a faculty advisor approved by the EECS department. This project is documented in a report and presented to a faculty committee of at least three graduate faculty members approved by the EECS department in a final oral exam. A manuscript with the student as a co-author with significant contribution that was submitted, accepted, or published in some reputable journals or conferences is acceptable as an alternative to the MS project report. 
Foundation Courses (at least 12 credit hours)
CMP_SC 7720Introduction to Machine Learning and Pattern Recognition3
or ECE 7720 Introduction to Machine Learning and Pattern Recognition
CMP_SC 7750Artificial Intelligence I3
or ECE 7750 Artificial Intelligence I
CMP_SC 7770Introduction to Computational Intelligence3
or ECE 7870 Introduction to Computational Intelligence
CMP_SC 8725Supervised Learning3
or ECE 8725 Supervised Learning
CMP_SC 8735Unsupervised Learning3
or ECE 8735 Unsupervised Learning
CMP_SC 8750Artificial Intelligence II3
CMP_SC 8770Neural Networks3
or ECE 8890 Neural Networks
CMP_SC 8001Advanced Topics in Computer Science (Topic: Deep Learning)1-4
or ECE 8001 Advanced Topics in Electrical and Computer Engineering
Elective Courses
CMP_SC 7010Computational Methods in Bioinformatics3
CMP_SC 7540Neural Models and Machine Learning3
or ECE 7540 Neural Models and Machine Learning
CMP_SC 7650Digital Image Processing3
or ECE 7655 Digital Image Processing
CMP_SC 7730Building Intelligent Robots4
or ECE 7340 Building Intelligent Robots
CMP_SC 7740Interdisciplinary Introduction to Natural Language Processing3
CMP_SC 7820Introduction to Digital Signal Processing3-4
or ECE 7830 Introduction to Digital Signal Processing
ISE 7410Data Analytics and Machine Learning for Operations Intelligence3
ISE 7565Smart Manufacturing Systems3
CMP_SC 8085Problems in Computer Science (at most 3 credit hours)3
ECE 8010Supervised Study in Electrical Engineering (at most 3 credit hours)3
CMP_SC 8130Computational Genomics3
CMP_SC 8150Integrative Methods in Bioinformatics3
CMP_SC 8180Machine Learning Methods for Biomedical Informatics3
CMP_SC 8370Data Mining and Knowledge Discovery3
CMP_SC 8580Machine Learning in Neuroscience3
or ECE 8580 Machine Learning in Neuroscience
CMP_SC 8650Advanced Image Processing3
or ECE 8855 Advanced Image Processing
CMP_SC 8675Biomedical Image Processing3
or ECE 8675 Biomedical Image Processing
CMP_SC 8690Computer Vision3
or ECE 8690 Computer Vision
CMP_SC 8740Advanced Natural Language Processing3
CMP_SC 8790Filtering, Tracking and Data Fusion3
CMP_SC 8810Advanced Digital Signal Processing3
or ECE 8810 Advanced Digital Signal Processing
CMP_SC 8062Probability and Stochastic Processes for Engineers3
or ECE 8860 Probability and Stochastic Processes for Engineers
CMP_SC 8870Modeling and Management of Uncertainty3
or ECE 8870 Modeling and Management of Uncertainty
MAE 8930Advanced Mechanical System Modeling and Optimization3
CV_ENG 8106Advanced Intelligent Transportation Systems3
Research Courses
CMP_SC 8980Research Masters Project in Computer Science
or ECE 8980 Research-Master Project in Electrical and Computer Engineering
CMP_SC 8990Research-Masters Thesis Computer Science
or ECE 8990 Research-Master Thesis in Electrical and Computer Engineering