The Master of Science in Artificial Intelligence (MSAI) is a professional degree that prepares engineers, applied scientists and technical professionals for career advancement in advanced technical and technical leadership roles in the rapidly growing field of artificial intelligence engineering. The core curriculum addresses a breadth of areas central to AI engineering expertise including machine learning, statistical learning, data mining and ethics.
The MSAI is offered both residentially on the Boulder campus and online through the Coursera Learning Management System (LMS) platform.
Requirements
Program Requirements
Degree Plan
As a course-based professional MS program, this is considered a Plan II: Non-Thesis program.
Students must complete a total of 30 credit hours of approved graduate-level course work with a grade of C or better and a cumulative GPA of at least 3.00.
Additionally, students must earn a B or better grade in all Breadth Requirement courses and Depth Requirement courses.
Course Requirements
The following requirements are subject to change; for the most current information, visit the department's Professional MS in Artificial Intelligence Degree Program Requirements.
| Code | Title | Credit Hours |
|---|---|---|
| Breadth Requirement | ||
| Breadth requirements are designed to accommodate students from a wide variety of academic backgrounds while ensuring that all students gain a common core of knowledge. Students are recommended to complete the breadth requirement in the first year. All students must earn a B or better in these courses. | ||
| Students must complete the following breadth requirement for a total of 12 credit hours. 3 credit hours will come from three 1-credit hour courses in AI Ethics, AI Toolkits, and AI Professional Skills. | 12 | |
| Machine Learning | ||
| Fundamentals of Neural Networks and Deep Learning | ||
| AI Engineering: Building, Scaling, and Deploying Large-Scale Models | ||
Three 1-credit hour courses in AI Ethics, AI Toolkits, and AI Professional Skills | ||
| Depth Requirement | ||
| Students must complete 12 credit hours from the following depth course list. All students must earn a B or better in these courses. | ||
| Choose four: | 12 | |
| Introduction to Robotics | ||
| Advanced Robotics | ||
| Algorithmic Human-Robot Interaction | ||
| Network Analysis and Modeling | ||
| Data Mining | ||
| Computer Vision | ||
| Natural Language Processing | ||
| Deep Reinforcement Learning | ||
| Information Theory, Statistical Inference, and Experimental Design | ||
| Data-Centric Computer Vision | ||
| Current Topics in Computer Science (Neuro-Symbolic NLP) | ||
| Current Topics in Computer Science (NLP for Cultural Analytics) | ||
| Current Topics in Computer Science (Deep Language Understanding) | ||
| Current Topics in Computer Science (Systems for Machine Learning) | ||
| Current Topics in Computer Science (Geospatial and Statistical Machine Learning) | ||
| Current Topics in Computer Science (Vision Language Models for Robotics) | ||
| Current Topics in Computer Science (Physical Human-Robot Interaction) | ||
| Seminar on Algorithmic Economics and Machine Learning | ||
| Theory of Machine Learning | ||
| Open Topics in Applied Mathematics (Convex Optimization) | ||
| Digital Image Processing | ||
| Digital Video | ||
| Elective Requirement | ||
| Students must complete 6 credit hours in addition to the breadth and depth requirements. Electives are not required to be selected from the depth course list. | ||
| An additional 6 credit hours are required to complete the degree, with restrictions. 1 | 6 | |
| Total Credit Hours | 30 | |
| 1 | Certain courses are not eligible to count to the MSAI degree program. Consult the program website for the most up-to-date information. |
Learning Outcomes
By the completion of the program, students will be able to:
- Demonstrate an understanding of the mathematical and computational foundations of AI.
- Design state of the art AI techniques to solve problems of relevance to industry and society at large.
- Use existing AI tools and techniques with an expert understanding of the principles behind their design and operation, and advance new AI tools and techniques to push the boundaries of AI.
- Apply AI techniques to diverse areas including healthcare, finance, education, engineering design and government.
- Keep up with the evolution of AI technology and maintain a lifelong professional readiness to adapt with the changing technology landscape.
- Appreciate the ethical implications of AI technology and the potential pitfalls behind specific deployments of AI techniques.