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Master of Science in Artificial Intelligence

• https://www.gvsu.edu/computing/artificial-intelligence-ms-106
• https://www.gvsu.edu/academics/artificial-intelligence-ai-ms.htm

Degree Overview

The program consists of 11 three-credit courses (33 credit hours), consisting of core courses, an area of specialization (track), electives, and a capstone option - an internship, project, or thesis. Classes are offered fully online, in a hybrid format, or in-person in the late afternoon or evening in downtown Grand Rapids.

Curriculum

• Core
• Tracks (technical and non-technical)
• Electives
• Capstone

Note: If students have already completed 12 credit hours of 4xx/5xx dual-listed courses, they must not take additional 4xx/5xx dual-listed courses to fulfill the elective course requirement. An asterisk (*) designates classes that are dual-listed courses.

International Partnership students: please review your school's agreement and communicate with your Graduate Program Director (GPD) about transferring in courses to GVSU, if applicable.

Technical and Non-Technical Tracks

The MS in Artificial Intelligence program offers two distinct tracks to accommodate students with diverse academic and professional backgrounds. The technical track is designed for students with prior experience in computing or related fields and focuses on the development, training, evaluation, and deployment of AI systems, including machine learning, data engineering, and AI-powered system design. The non-technical track is intended for students without a technical background who seek to effectively understand and apply AI in their respective domains. This track emphasizes prompt engineering, ethical considerations, bias and fairness, human-centered AI, and best practices for responsible AI use. Together, these tracks ensure that all students are equipped to engage with AI technologies thoughtfully and effectively.

Core

Required:

• AI 501 - Introduction to Artificial Intelligence (3 credits)
• CIS 635 - Knowledge Discovery and Data Mining (3 credits)

Choose 2 of the following:

• *AI 502 - Generative Artificial Intelligence (3 credits)
• *AI 511 - AI Ethics and Bias (3 credits)
• *AI 545 - Machine Learning Operations (3 credits)
• CIS 678 - Machine Learning (3 credits)
• SE 511 - Introduction to Software Engineering (3 credits)

Tracks

Students must complete one of the following tracks, taking 4 courses total:

Artificial Intelligence Systems (technical)

Required

• *AI 545 - Machine Learning Operations (3 credits)
• *SE 513 - Software Testing (3 credits)

Choose 2 of the following

• *AI 502 - Generative Artificial Intelligence (3 credits)
• *AI 521 - Applied Computer Vision (3 credits)
• *AI 531 - Natural Language Processing (3 credits)
• *AI 541 - Edge AI (3 credits)
• *AI 551 - Reinforcement Learning (3 credits)
• CIS 641 - Systems Analysis and Design (3 credits)
• SE 512 - Requirements Specification (3 credits)
• *SE 530 - Software Construction and Delivery (3 credits)
• *SE 536 - Data Analytics in Software Engineering (3 credits)
• *SE 537 - Software Quality Assurance (3 credits)

Cybersecurity and Artificial Intelligence (technical)

Choose 4 of the following:

• *AI 511 - AI Ethics and Bias (3 credits)
• *CIS 518 - Secure Software Engineering (3 credits)
• CYB 616 - Cybersecurity Fundamentals (3 credits)
• CYB 619 - Artificial Intelligence and Data Analytics for Cybersecurity (3 credits)
• *CYB 520 - Trustworthy AI (3 credits)

Edge and Cloud Artificial Intelligence (technical)

Required

• *AI 541 - Edge AI (3 credits)

Choose 3 of the following

• *AI 521 - Applied Computer Vision (3 credits)
• CIS 554 - Computer Networking (3 credits)
• CIS 655 - Cloud Applications Development (3 credits)
• *SE 531 - Software Virtualization (3 credits)

Human Centered Artificial Intelligence (non-technical)

Required

• *HCC 531 - Human AI Interaction (3 credits)

Choose 3 of the following

• *AI 502 - Generative Artificial Intelligence (3 credits)
• *AI 521 - Applied Computer Vision (3 credits)
• *AI 551 - Reinforcement Learning (3 credits)
• CIS 671 - Information Visualization (3 credits)
• *HCC 502 - User Experience Research (3 credits)

Electives

Students must complete at least three elective credits. Three additional elective credits will be required if the project option is chosen to meet the capstone requirement instead of the thesis option.

Capstone

Students must complete either a 3-credit project course, or a 3-credit thesis course taken twice.

• CIS 693 - Master's Project (3 credits)

OR

• CIS 695 - Master's Thesis (3 credits)

An internship option must be discussed with the Graduate Program Director.

Study Plan

Before starting the program, students must have a study plan approved by the Graduate Program Director. Students are responsible for keeping the plan of study current before the beginning of each semester and getting the GPD's approval for any changes to the plan.