2025-2026 Academic Catalog
Applied Artificial Intelligence, MSAAI
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Master of Science Applied Artificial Intelligence Overview
The Master of Science in Applied Artificial Intelligence (MSSAI) program provides students with a strong foundation in both the theoretical principles and practical applications of AI. The curriculum emphasizes applied learning, integrating core AI concepts such as machine learning, deep learning, and data mining with ethical considerations. Students will learn to design and deploy AI-driven solutions, leveraging advanced techniques to solve real-world problems across various industries. Through hands-on projects and specialized electives, students will also gain expertise in high-demand areas like generative AI and visual computing. In addition to mastering AI methodologies, students will develop key skills in ethical decision-making, leadership, and communication. They will be prepared to lead AI projects, manage cross-disciplinary teams, and communicate complex AI insights to both technical and non-technical audiences. The program culminates in a capstone practicum, where students apply their knowledge to real-world challenges, ensuring they graduate ready to drive innovation and deliver impactful AI solutions in diverse professional environments. Program Learning Outcomes
Upon completing the MSSAI program, graduates will be able to: -
Demonstrate a comprehensive understanding, through explanation and application, of the core principles, techniques, and algorithms of artificial intelligence, including machine learning, deep learning, and data mining. -
Design and deploy AI-driven solutions to address real-world problems across various industries by leveraging advanced AI methodologies and tools. -
Implement data mining techniques to extract insights and drive decision-making in AI systems, applying them in diverse domains. -
Assess and apply ethical principles in the development, deployment, and governance of AI technologies, promoting responsible and equitable use of AI in society. -
Lead and manage AI projects, collaborating across teams and disciplines to integrate AI systems in broader organizational or societal contexts effectively. -
Develop expertise in one or more specialized AI domains, such as generative intelligence or visual computing, to address domain-specific challenges with advanced AI techniques. -
Effectively communicate complex AI-driven insights and recommendations to both technical and non-technical audiences, using clear and compelling data visualizations and storytelling techniques. Course of Study
The MSSAI program requires 10 courses: 7 required core courses + 3 electives from a diverse selection of specialized courses or 1 concentration made up of 3 courses. In addition, students are required to complete an internship. Internship
Completing an internship is a requirement in this graduate program. Students are responsible for securing their own internship. While students will gain valuable experience, they will not earn academic credit for their internship. Internship requirements: - Must include a minimum of 120 hours
- Must be directly related to the student’s program of study
- Must be approved by the Director of Career and Professional Development
Exceptions: - Students who have seven (7) or more years of professional work experience directly related to their program of study may seek to waive the internship requirement.
- Students who currently work full-time in a field/industry directly related to their program of study.
Students seeking to waive the internship requirement must receive permission from the Director of Career and Professional Development by the end of their second semester in the master’s program. Review the Internship Handbook for more information. Please connect with your academic advisor to plan your studies accordingly. MSAAI Concentrations
Students may choose to specialize by completing a concentration, each consisting of three specific courses. Concentrations allow students to gain in-depth expertise in a focused area of applied AI. To declare a concentration, consult with your academic advisor. Generative Intelligence and Applications (GIA)
The Generative Intelligence and Applications (GIA) concentration equips students with advanced knowledge and practical skills in cutting-edge generative AI models and technologies. Focusing on systems that generate content, enable intelligent decision-making, and drive innovation, the concentration explores key areas such as Natural Language Processing, Reinforcement Learning, and large language models. These technologies form the backbone of modern AI applications in creativity, automation, and human-like intelligence. Students will gain hands-on experience building, fine-tuning, and deploying generative models for real-world use in areas such as text generation, conversational AI, and multimodal AI applications. Blending theory with applied projects, the GIA concentration prepares students to develop generative AI solutions across a range of industries, including finance, healthcare, creative media, gaming, and enterprise automation. GIA Required Courses: MSAI 3120 - Reinforcement Learning MSDA 3051 - Natural Language Processing (NLP) MSAI 3125 - Large Language Models (LLM’s) GIA Career Pathways: Generative AI Engineer NLP Engineer AI Research Scientist Machine Learning Engineer specializing in LLMs AI for Visual Computing and Imaging
The AI-VCI concentration equips students with the expertise to develop and deploy advanced computer vision and image processing solutions. This concentration emphasizes AI techniques that enable machines to analyze, interpret, and generate insights from visual data. Students will delve into deep learning models for image recognition, object detection, and image synthesis, gaining hands-on experience in real-world applications across diverse fields such as healthcare, robotics, security, augmented reality, and more. Through a blend of theoretical concepts and practical projects, students will learn to apply machine learning and deep learning models to visual data. They will acquire skills in computer vision, image processing, and machine learning for visual recognition, preparing them to work with AI-driven solutions in autonomous systems, medical imaging, smart surveillance, and beyond. AI-VCI Required Courses: MSAI 3130 - Practical Computer Vision with AI MSAI 3135 - Image Processing MSAI 3140 - Machine Learning for Computer Vision AI-VCI Career Pathways: Computer Vision Engineer Machine Learning Engineer specializing in Vision AI AI Research Scientist in Visual Computing Autonomous Systems Engineer Medical Imaging AI Specialist AI Consultant for AR/VR Technologies Non-Concentration Path
Alternatively, students may pursue a flexible, non-concentration path by selecting any three courses from the “Elective Courses” list. This approach allows them to customize their studies to align with their individual interests and career goals without declaring a specific concentration. |
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