Master of Science in Applied Artificial Intelligence (online)
Mission
The purpose of the MS in Applied AI program is to train individuals to evaluate, apply, and advance AI techniques in their professional field. Through this program, students will have the chance to use and apply AI skills and tools to address real-world challenges in industries such as social media, healthcare, business, and e-commerce.
Program Educational Objectives
The MS in AAI Program shall:
- Enable professionals to use Artificial Intelligence tools and methods in their fields to solve problems and improve the efficiency of their processes.
- Apply AI techniques to solve real-world problems in business and e-commerce, healthcare, digital humanities, and more.
Student Outcomes
The MS in AAI Program shall:
- Equip graduates with advanced knowledge and skills in applying Artificial Intelligence techniques, enabling them to advance their careers.
- Prepare graduates for a career shift into Artificial Intelligence within their professions.
- Provide graduates with the ability to pursue their Doctoral studies in artificial intelligence related to their field of expertise and profession. development.
Admission Requirements
The requirements for admission include having earned a bachelor’s degree from a regionally accredited institution. The bachelor’s degree final cumulative GPA must be at least 2.5 on a 4.0 scale or its equivalent. The candidate’s work history, and other personal qualities will be considered if the GPA requirement is not met.
Applicants from programs where English is not the language of instruction must submit proof of proficiency by passing either IELTS, TOEFL iBT, or Duolingo English Test with the minimum scores respectively of 6.5, 80, and 115.
Curriculum
A total of 30 credits are required as follows:
- Core Requirements (18 credits)
- Thesis or Project (3 or 6 credits)
- Elective Courses (6 or 9 credits)
Core Requirements (18 credits)
- AAI601 Mathematics for Applied AI (3 credits)
- AAI602 Programing for Applied AI (3 credits)
- AAI611 Machine Learning Fundamentals and Applications (3 credits)
- AAI612 Deep learning and its Applications (3 credits)
- AAI613 Computer vision and its Applications (2 credits)
- AAI614 Data Science and its Applications (3 credits)
- AAI615 Ethics and AI (1 credit)
Thesis or Project (3 or 6 credits)
Elective Courses (6 or 9 credits)
- AAI631 Data Visualization (2 credits)
- AAI632 Big Data Analytics (2 credits)
- AAI633 Introduction to Generative AI (2 credits)
- AAI634 Data Engineering (1 credit)
- AAI635 Recommender Systems (1 credit)
- AAI641 Healthcare Analytics (3 credits)
- AAI642 AI for Biomedical Informatics (3 credits)
- AAI643 AI for Medical Diagnosis and Prediction (3 credits)
- AAI651 AI Methods for Natural Language Processing (2 credits)
- AAI652 AI for Digital Humanities (3 credits)
- AAI653 AI Driven Design Thinking (3 credits)
- AAI681 Special Topics in Applied AI (1-3 credits) (*)
- DSC653 Bridging Search and Generation in AI with RAG (1 credit)
Tracks
- AI in Business and e-commerce
- AI in Healthcare
- AI in Digital Media