Arizona State University Ira A. Fulton Schools of Engineering 21 courses 30 total credits
The Master of Science program in artificial intelligence prepares you for either professional practice or further graduate doctorate-level study in the rapidly growing field. The program helps you develop proficiency in both theoretical and practical AI research aimed at enabling you to independently analyze, evaluate and invent new concepts for designing efficient and reliable AI systems. You have the opportunity to work with highly active faculty research groups, gaining expertise in the cutting-edge of AI while simultaneously developing the skills to invent and deploy novel AI algorithms and systems. The curriculum encompasses a core of foundational courses in AI, complemented by electives from three prominent AI streams: statistical learning; embodied AI, cognition and perception; and reasoning and decision making. Furthermore, you learn how to conduct original research, culminating in a research-oriented thesis that advances knowledge in AI, or you learn how to develop professional-grade systems founded on AI algorithms and systems thinking. You are able to develop new AI paradigms, applications and systems, and advance knowledge of the field itself. GI Bill® benefitsThis new program is not yet approved for use with GI Bill® benefits. GI Bill® is a registered trademark of the U.S. Department of Veterans Affairs. More information about education benefits offered by VA is available at the official U.S. government website at https://www.benefits.va.gov/gibill/.
| Required Core | 6 credits |
| Statistical Learning Restricted Electives | 3 credits |
| Embodied AI, Cognition and Perception Restricted Electives | 3 credits |
| Reasoning and Decision Making Restricted Electives | 3 credits |
| Electives | 15 credits |
| Required Core | 6 credits |
| Statistical Learning Restricted Electives | 3 credits |
| Embodied AI, Cognition and Perception Restricted Electives | 3 credits |
| Reasoning and Decision Making Restricted Electives | 3 credits |
| Total Credits | 30 credits |
Required Core
Students choose two of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 550 | Combinatorial Algorithms and Intractability | 3 |
| CSE 551 | Foundations of Algorithms | 3 |
| CSE 552 | Randomized and Approximation Algorithms | 3 |
| CSE 555 | Theory of Computation | 3 |
| CSE 571 | Artificial Intelligence | 3 |
| Total Required Core | 6 credits | |
Statistical Learning Restricted Electives
Students choose one of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 524 | Machine Learning Acceleration | 3 |
| CSE 569 | Fundamentals of Statistical Learning and Pattern Recognition | 3 |
| CSE 572 | Data Mining | 3 |
| CSE 573 | Semantic Web Mining | 3 |
| CSE 575 | Statistical Machine Learning | 3 |
| Total Statistical Learning Restricted Electives | 3 credits | |
Embodied AI, Cognition and Perception Restricted Electives
Students choose one of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 509 | Digital Video Processing | 3 |
| CSE 515 | Multimedia and Web Databases | 3 |
| CSE 522 | Real-time Embedded Systems | 3 |
| CSE 570 | Advanced Computer Graphics I | 3 |
| CSE 576 | Topics in Natural Language Processing | 3 |
| Total Embodied AI, Cognition and Perception Restricted Electives | 3 credits | |
Reasoning and Decision Making Restricted Electives
Students choose one of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 556 | Game Theory and Application to Networks | 3 |
| CSE 561 | Modeling and Simulation Foundations for Digital Twins | 3 |
| CSE 574 | Planning and Learning Methods in AI | 3 |
| CSE 579 | Knowledge Representation and Reasoning | 3 |
| Total Reasoning and Decision Making Restricted Electives | 3 credits | |
Electives
Required Core
Students choose two of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 550 | Combinatorial Algorithms and Intractability | 3 |
| CSE 551 | Foundations of Algorithms | 3 |
| CSE 552 | Randomized and Approximation Algorithms | 3 |
| CSE 555 | Theory of Computation | 3 |
| CSE 571 | Artificial Intelligence | 3 |
| Total Required Core | 6 credits | |
Statistical Learning Restricted Electives
Students choose one of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 524 | Machine Learning Acceleration | 3 |
| CSE 569 | Fundamentals of Statistical Learning and Pattern Recognition | 3 |
| CSE 572 | Data Mining | 3 |
| CSE 573 | Semantic Web Mining | 3 |
| CSE 575 | Statistical Machine Learning | 3 |
| Total Statistical Learning Restricted Electives | 3 credits | |
Embodied AI, Cognition and Perception Restricted Electives
Students choose one of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 509 | Digital Video Processing | 3 |
| CSE 515 | Multimedia and Web Databases | 3 |
| CSE 522 | Real-time Embedded Systems | 3 |
| CSE 570 | Advanced Computer Graphics I | 3 |
| CSE 576 | Topics in Natural Language Processing | 3 |
| Total Embodied AI, Cognition and Perception Restricted Electives | 3 credits | |
Reasoning and Decision Making Restricted Electives
Students choose one of the following courses:
| Code | Course Title | Credits |
|---|---|---|
| CSE 556 | Game Theory and Application to Networks | 3 |
| CSE 561 | Modeling and Simulation Foundations for Digital Twins | 3 |
| CSE 574 | Planning and Learning Methods in AI | 3 |
| CSE 579 | Knowledge Representation and Reasoning | 3 |
| Total Reasoning and Decision Making Restricted Electives | 3 credits | |
Culminating Experience (3 or 6 credit hours)
| Code | Course Title | Credits |
|---|---|---|
| CSE 593 | Applied Project | 3 |
| CSE 599 | Thesis | 6 |
Additional Curriculum Information
This program offers a course-based option. A course-based master's option is best suited for students who want to deepen their knowledge and advance their professional skills through structured coursework rather than independent research. Students who enjoy solving real-world problems and applying practical skills in professional settings may be drawn to the applied project option. Students with a strong interest in research and a desire to contribute original knowledge to their field may be well-suited for the thesis option. Students select one of the listed courses in each of the required core, statistical learning restricted electives, embodied AI, cognition and perception restricted electives, and reasoning and decision making restricted electives course areas. Courses should be selected in consultation with the program advisor. Students who have not completed a formal AI course are strongly encouraged to complete CSE 571 as part of their core.
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