The 30-credit Master of Science in Artificial Intelligence includes 21 credits of required courses, 3 credits of electives, and 6 credits of thesis research. Courses are linked below to the University Bulletin. All courses offered are three credits unless otherwise indicated.
Master of Science in AI Required Courses
A-I 500 - Quantitative Methods or STAT 500 - Applied Statistics
A-I 801 - Foundations of Artificial Intelligence
A-I 570 - Deep Learning (formerly DAAN 570)
Prerequisite: A-I 500
A-I 572 - Reinforcement Learning
A-I 574 - Natural Language Processing
Prerequisite: A-I 570
IE 575 - Foundations of Predictive Analytics
A-I 501 Interdisciplinary Research Design for Artificial Intelligence
RECR (Responsible and Ethical Conduct of Research) Required Online Activity
You must successfully complete the RECR requirement of the Penn State degree in order to graduate. The RECR Module is offered only in spring and fall semesters.
M.S. in AI Electives
Select 3 credits of electives from a list of approved courses maintained by the program office, including:
M.S. in AI Thesis
A-I 600 Thesis Research (6 credits)
You will write a thesis paper based on your applied research project, which will include a project description, analysis, and interpretation of your findings. You will be encouraged to upload your thesis report to be publicly available via ScholarSphere and to participate in research poster competitions.
Course Schedules
Contact [email protected] with general questions. Contact your assigned faculty advisor for advice on course selection.
GPA Requirements
Students must maintain a minimum grade point average of 3.0 (B) throughout the program. A 3.0 cumulative GPA is required to graduate.
M.S. in AI Learning Goals
After completing this program, graduates will be able to:
- Explain and compare state-of-the-art architectures in deep learning, reinforcement learning, and natural language processing, identifying their assumptions and limitations
- Validate rigorous command of statistical learning theory, optimization, and probabilistic modeling as the foundations of modern AI
- Apply advanced analytical reasoning to scrutinize model assumptions, performance metrics, and reproducibility, recommending refinements where necessary
- Design, implement, and empirically evaluate novel AI models to answer open research questions, selecting tools and benchmarks appropriate to the problem
- Plan and conduct end-to-end empirical studies—data acquisition, preprocessing, experiment design, analysis—using reproducible research practices
- Apply ethical reasoning frameworks to assess societal impacts, ensuring AI solutions align with fairness, transparency, and accountability principles
- Produce clear, journal-ready manuscripts and technical reports conveying research context, methods, results, and implications, and including data sources, AI related technologies, and evaluation metrics, suitable for replication and audit
- Be involved in lifelong learning by reflecting on emerging AI trends and tools, participating in seminars and workshops, and continuously updating their technical and professional competencies
