Artificial Intelligence and Machine Learning MSAIML
Degree Awarded: MS in AI & Machine Learning (MSAIML)
Minimum Required Credits: 30.0
Co-op Option: Available for full-time, on-campus master's-level students
Classification of Instructional Programs (CIP) code: 11.0701
Standard Occupational Classification (SOC) code: 15-0000
About the Program
The Master of Science in Artificial Intelligence and Machine Learning (MSAIML) provides a strong foundation in the artificial intelligence and machine learning fields, with a focus on mathematical foundations, algorithms, tools, and applications as they pertain to artificial intelligence and machine learning. Students gain competency in fundamental methods and techniques in artificial intelligence and machine learning. Their fundamental understanding is applied to real data sets and data analysis tasks with the help of state-of-the-art technologies, tools, and platforms. The MSAIML culminates with a capstone experience where students work on a real world or research problem using the knowledge they have gained throughout the program.
Additional Information
For more information about this program, please contact the School of Computer and Information Sciences
Degree Requirements
| Core Courses | ||
| CS 5510 | Principles of Artificial Intelligence | 3.0 |
| CS 5520 | Fundamental Machine Learning | 3.0 |
| CS 6550 | Modern AI Application Development | 3.0 |
| CS 7950 | Artificial Intelligence and Machine Learning Capstone | 3.0 |
| IS 5610 | Applications of Artificial Intelligence | 3.0 |
| Subject-Area Focused Courses | ||
| Select two (2) of the following courses: | 6.0 | |
| Reinforcement Learning | ||
| Computer Vision | ||
| Equity and Explainability in Machine Learning | ||
| Game Artificial Intelligence | ||
| Neuro-symbolic Collective Intelligence | ||
| Algorithmic Game Theory | ||
| Embodied AI | ||
| Foundations of Deep Learning | ||
| Natural Language Processing | ||
| Computational Network Neuroscience | ||
| Topics in Artificial Intelligence | ||
| Electives | ||
| Select three (3) additional courses from the following options: | 9.0 | |
Any CS (Computer Science) course in the list above | ||
| Pattern Recognition | ||
| Bioinformatics | ||
| Statistical Analysis of Genomics | ||
| Image Processing | ||
| Cell and Tissue Image Analysis | ||
| Neuromorphic Computing | ||
| Optimization Methods for Engineering Design | ||
| Multimedia Forensics and Security | ||
| Detection and Estimation Theory | ||
| Information Theory and Coding | ||
| Principles of Optimization and Inference | ||
| Applied Machine Learning | ||
| Applied Deep Learning | ||
| Deep Learning Architectures | ||
| User Modeling and Recommender Systems | ||
| Modeling Natural Language | ||
| Agile and AI-Enabled Project Leadership | ||
| Human-Artificial Intelligence Interaction | ||
| Intelligent Search and Language Models | ||
Any other CS (Computer Science) course at the 5000-7999 level with CS department approval | ||
Any other IS (Information Science), ECE (Electrical and Computer Engineering), MATH (Mathematics), SE (Software Engineering), or other course at the 5000-7999 level with CS department approval | ||
Up to 6 credits of independent study or thesis coursework with CS department approval | ||
| Optional Co-op Experience | ||
| Co-op is an option for this degree for full-time on-campus students. Students choosing this option will be required to complete COOP 5000 as preparation for their co-op experience. | ||
| Total Credits | 30.0 | |
Program Learning Outcomes
- Analyze a problem and identify and define the use of artificial intelligence and/or machine learning (AI/ML) as appropriate to its solution
- Interpret and communicate the output of statistical and algorithmic methods
- Demonstrate the ability to collaborate on a team to design and implement a computer-based AI/ML system
- Apply mathematical foundations, algorithmic principles, and computational knowledge in the modeling and design of AI/ML systems in a way that
- Demonstrates comprehension of the tradeoffs involved in design choices
- Analyze, design, implement, and evaluate a computer-based AI/ML system, process, component, or program to meet desired needs
- Interpret and communicate existing research papers in the field of artificial intelligence
- Describe and apply principles of responsible AI