In addition to meeting the requirements of the university and of the College of Engineering, students must meet the requirements specified below.
Admission
For admission in the MS in Artificial Intelligence program, students must have a strong background in programming, mathematics and statistics. Students are expected to have a degree in a STEM discipline or a related field with experience in computer programming (CSE 231 and CSE 232) and algorithms (CSE 331), linear algebra (MTH 314), probability and statistics and multivariable calculus (MTH 234).
Requirements for Master of Science in Artificial Intelligence
Students must complete a minimum of 30 credit hours from the following:
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| 1. |
The following required courses (12 credits): |
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CSE |
807 |
Ethics of AI |
3 |
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CSE |
840 |
Computational Foundations in Artificial Intelligence |
3 |
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CSE |
841 |
Artificial Intelligence |
3 |
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MTH |
800 |
Quantitative Foundations for Machine Learning |
3 |
| 2. |
At least 3 of the following core courses: |
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CSE |
802 |
Pattern Recognition and Analysis |
3 |
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CSE |
803 |
Computer Vision |
3 |
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CSE |
842 |
Natural Language Processing |
3 |
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CSE |
847 |
Machine Learning |
3 |
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CSE |
849 |
Deep Learning |
3 |
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CSE |
851 |
Adversarial Machine Learning |
3 |
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MTH |
801 |
Machine Learning Algorithms: Mathematical Analysis |
3 |
| 3. |
At most 3 of the following elective courses (Student may choose to take additional core courses instead of elective courses): |
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CSE |
404 |
Introduction Machine Learning |
3 |
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CSE |
440 |
Introduction to Artificial Intelligence |
3 |
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CSE |
848 |
Evolutionary Computing |
3 |
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CSE |
850 |
Genetic Programming |
3 |
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CSE |
881 |
Data Mining |
3 |
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CSE |
830 |
Design and Theory of Algorithms |
3 |
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CSE |
885 |
Artificial Neural Networks |
3 |
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STT |
465 |
Bayesian Statistical Methods |
3 |
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STT |
810 |
Mathematical Statistics for Data Scientists |
3 |
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STT |
811 |
Applied Statistical Modeling for Data Scientists |
3 |
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STT |
812 |
Statistical Learning and Data Analysis |
3 |
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STT |
873 |
Statistical Learning and Data Mining |
3 |
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MTH |
844 |
Projects in Industrial Mathematics |
3 |
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MTH |
468 |
Predictive Analysis |
3 |
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MTH |
483 |
Mathematical Machine Learning |
3 |
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CMSE |
823 |
Numerical Linear Algebra/Numerical analysis |
3 |
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CMSE |
831 |
Computational Optimization |
3 |
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ECE |
855 |
Noncooperative game theory |
3 |
| 4. |
At most 6 credits of capstone/thesis. |
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Students will register for independent study with a faculty member (CSE 890/899, CMSE 891/899, STT 890/899, MTH 890/899). To register for capstone/thesis, students must submit a proposal to the director of the program at least 30 days before the start of the semester and have it approved. |
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| 5. |
If a student is interested in taking other courses (including special topics courses) in place of core or elective courses, they should get approval from the director at least 30 days before the start of the semester when the course is offered. |
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| 6. |
Students cannot take more than 6 credits at 400-level. All 400-level courses must be approved by the director and are only intended for students who lack a background in the relevant area. |
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