Introduction
The Master of Science in Applied Artificial Intelligence (MSAAI) at Neumont is a dynamic blend of AI foundations, theory, real-world applications, and applied business strategies. Our project- and problem-based curriculum is crafted to equip students with the skills most sought after by today's top employers. Graduates are proficient in AI concepts, tools, and emerging industry trends.
Program Benefits:
- Graduates are prepared to optimize solutions for current workplace challenges and adapt to evolving market demands.
Program Overview
This robust 36-credit program integrates Machine Learning, Prompt Engineering, and various productivity tools into a comprehensive AI education. Our hands-on approach includes:
- Active learning techniques such as lectures, discussions, debates, presentations, and creative assessments.
- Extensive lab work and assignments to deepen technical understanding.
Team-based projects where students apply theoretical knowledge in practical settings.
Program Objectives
Graduates of the MSAAI are equipped to:
- Design and implement significant AI projects.
- Utilize Generative AI to enhance business operations.
- Analyze the impact of AI on workflow efficiencies.
- Explore diverse AI fields and their applications.
- Adopt AI technologies effectively in new work environments.
- Process and analyze large data sets with precision.
- Employ advanced data analysis techniques for business insights.
- Integrate AI technologies into existing platforms effectively.
- Develop and refine Machine Learning models.
- Communicate professionally and effectively.
- Create high-quality technical and non-technical documentation.
- Collaborate effectively in team settings.
- Solve problems critically and innovatively.
- Apply learned skills in both personal and professional contexts.
MSAAI Program Requirements
| Computer Science Core Courses |
15 Credits |
| Business Information Technology Courses |
6 Credits |
| Lab Courses |
12 Credits |
| Capstone Project Courses |
3 Credits |
| Total Required for MS in Applied Artificial Intelligence |
36 Credits |
Introductory Courses (6 credits)
| BIT510 | Programming for Artificial Intelligence | 3 credits |
| CSC505 | Introduction to Artificial Intelligence | 3 credits |
Computer Science Core Courses (9 credits)
| CSC525 | Machine Learning I | 3 credits |
| CSC535 | Machine Learning II | 3 credits |
| CSC545 | AI Tools and Practical Applications | 3 credits |
Business Information Technology Core Courses (6 credits)
| BIT515 | Data Analysis and Interpretation | 3 credits |
| BIT555 | Survey of Additional AI Topics | 3 credits |
Lab Courses (12 credits)
| BIT515P | Data Analysis and Interpretation Project | 3 credits |
| CSC525P | Machine Learning I Tools and Project | 3 credits |
| CSC535P | Machine Learning II Tools and Project | 3 credits |
| CSC545P | AI Tools and Practical Applications Project | 3 credits |
Capstone (3 credits)
| PRO590 | AI Capstone Project | 3 credits |