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Overview

The M.S. in Artificial Intelligence delivers a rigorous, end-to-end foundation in modern AI, spanning mathematical principles, computational methods, and the frontier systems transforming industry and research. Students develop strong grounding in computational statistics, linear algebra, and numerical methods before advancing into machine learning, neural networks, and deep learning to design and deploy intelligent tools, systems, and applications. They gain hands-on experience with GPU-accelerated architectures and transformer-based models that power large language models, retrieval-augmented generation (RAG), multimodal reasoning, and emerging pathways toward AGI. The curriculum also emphasizes modern intelligent database systems and scalable data infrastructures, preparing students to architect platforms that support real-time inference, semantic retrieval, and complex AI workflows at scale.

Designed for both industry impact and cutting-edge research, the program positions students at the forefront of AI, including spatial intelligence, digital twins, AI-augmented software engineering, and secure multi-agent systems. Its multidisciplinary approach bridges computing with business, healthcare, robotics, finance, and other domains, enabling students to translate AI innovation into tangible, high-impact applications. By integrating responsible AI principles with advanced agentic workflows and AI-augmented development practices, the program equips students not only to build intelligent systems, but also to elevate productivity, decision-making, and organizational efficiency. The culminating capstone synthesizes theory and practice through original research, industry collaboration, or deployable AI solutions, graduating forward-looking innovators prepared to lead in the rapidly evolving AI-driven economy.

The 36-credit master's in AI can be completed full time in just 21 months or part time at a pace that makes sense for you. View a typical course sequence for full-time students. Part-time students select 1-2 courses per semester in consultation with their program director. Review degree requirements below, and download course descriptions here. 

Course Descriptions

Typical course sequence 

Degree Requirements 

To earn the M.S. in Artificial Intelligence, you must complete 24 credits of required foundational courses, including a capstone project, as well as 12 credits of elective courses, which may include an internship. Courses are 3 credits, unless otherwise noted. 

Core Requirements (24 credits) 

• AIM 5001 Data Acquisition and Management
• AIM 5002 Computational Statistics and Probability
• AIM 5003 Numerical Methods
• AIM 5004 Predictive Models
• AIM 5005 Machine Learning
• AIM 5000 Artificial Intelligence
• AIM 5007 Neural Networks and Deep Learning
• AIM 5008 AI Capstone: R&D Experience 

Electives (12 credits) 

• AIM 5009 Bayesian Methods
• AIM 5010 AI Product Studio
• AIM 5011 Natural Language Processing
• AIM 5012 Data Visualization
• AIM 5013 Advanced Data Engineering
• Complex Systems: Financial Time Series Analysis
• AIM 5012 Special Topics (1-3 credits)
• AIM 5999 Independent Study (1-3 credits)
• Internship (1-3 credits)*  

Note: Electives offerings will vary each semester. Therefore, some choices will not be available for a particular cohort. Summers include one live-online course and are primarily used for internships, studio, research and special topics. 

*Internship can be taken as an elective beginning in the summer semester.