Master of Artificial Intelligence Classes
Courses for the Master of AI are linked below to the University Bulletin. Please refer to the prerequisites mentioned on this page as those listed on the University Bulletin may differ from Penn State Great Valley. All courses offered are three credits unless otherwise indicated.
Required Master of Artificial Intelligence Courses
A-I 500 - Quantitative Methods
(Recommended for first semester)
A-I 801 - Foundations of Artificial Intelligence
(Recommended for first semester)
A-I 570 - Deep Learning (formerly DAAN 570)
Prerequisite: A-I 500
A-I 574 - Natural Language Processing
Prerequisite: A-I 570
A-I 804 - Ethics of Artificial Intelligence
IE 575 - Foundations of Predictive Analytics
Prerequisite: A-I 500
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.
Master of Artificial Intelligence Electives (select 9 credits)
A-I 572 - Reinforcement Learning
DAAN 822 - Data Collection and Cleaning
Prerequisite: A-I 500
DAAN 862 - Analytics Programming in Python
DAAN 881- Data-Driven Decision Making
Prerequisite: A-I 500
INSC 521 - Database Design Concepts
Required Master of AI Capstone Course (3 credits)
A-I 894 - Research Topic
Prerequisites: A-I 500, A-I 801, A-I 804, A-I 570, A-I 574, and IE 575. Available in fall semester only.
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.
Master of AI Learning Goals
Upon completion of Master of Artificial Intelligence program, graduates will be able to:
- Apply statistical reasoning, visualization and predictive analytics principles to collect, prepare, and model data, justifying method selection with reference to statistical assumptions and performance metrics.
- Assess and synthesize state-of-the-art artificial intelligence and related architectures and techniques in representational learning and statistical learning for solving complex, real-world problems.
- Be able to critically evaluate state-of-the-art AI techniques and technologies to design end-to-end AI systems for relevant real-world problems.
- Design, train, and evaluate AI models, applying best practices in hyperparameter tuning and model validation.
- Collect and pre-process diverse datasets, select and develop appropriate AI/ML algorithms, and implement end-to-end data-driven pipelines to solve real-world problems.
- Critically evaluate emerging AI trends and their strategic implications for different industries, applying analytical frameworks.
- 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.
