Course Listing
Introduction to Artificial Intelligence
ARIN 310 | 3 Credits
Course Desc: A comprehensive introduction to the basic principles and terminology of the field of artificial intelligence (AI). The aim is to use a solid understanding of AI concepts to facilitate informed decision-making and collaboration with technical teams. Topics include various subfields of AI, such as machine learning, natural language processing, and computer vision, as well as real world applications of AI in areas such as recommender engines, supply chains, fraud detection, and customer service.
Artificial Intelligence Applications
ARIN 320 | 3 Credits
Course Desc: (No programming or math background required.) An interactive, hands-on study of current artificial intelligence (AI) applications spanning multiple disciplines and domains, including business, science, communications, and computing. The goal is to use datasets with AI and machine learning applications from leading cloud vendors, including Amazon and Microsoft. Projects and laboratory exercises demonstrate how AI can be used to solve problems across a wide variety of disciplines. Students may receive credit for only one of the following courses: ARIN 320 or CMSC 307.
Generative AI
ARIN 340 | 3 Credits
Course Desc: A comprehensive introduction to generative artificial intelligence models, a cutting-edge area of AI that focuses on creating content such as images, music, and text. Topics include the underlying principles and techniques behind generative models, e.g., large language models. Emphasis is on practical applications that demonstrate how generative AI is revolutionizing industries such as art, music composition, and content creation. Discussion covers the creative potential of AI generative pretrained transformers. Hands-on experience with generative tools is provided.
Responsible AI
ARIN 350 | 3 Credits
Course Desc: An in-depth examination of the ethical considerations, societal impact, and responsible use of AI. The goal is to navigate the ethical landscape of AI, make informed decisions, and promote responsible AI practices within one's organization. Topics include bias and fairness in AI algorithms, transparency, privacy concerns, and the ethical implications of generative AI models. Real-world examples of AI-related ethical challenges are explored through case studies and discussions.
Artificial Intelligence in the Enterprise
ARIN 410 | 3 Credits
Course Desc: A project-based examination of the practical application of AI, transforming sectors such as finance, healthcare, marketing, and supply chain management. The aim is to identify opportunities for AI adoption in one's organization and leverage AI for strategic advantage. Topics include predictive analytics, recommendation systems, automated decision-making, and the integration of AI into business processes.
Advanced Machine Learning
ARIN 440 | 3 Credits
Course Desc: Prerequisites: DATA 230 and DATA 430. A project-based study of advanced concepts and applications in machine learning (ML), such as neural networks, support vector machines (SVM), ensemble models, deep learning, and reinforced learning. Emphasis is on building predictive models for practical business and social problems, developing complex and explainable predictive models, assessing classifiers, and comparing their performance. All stages of the ML life cycle are developed, following industry best practices for selecting methods and tools to build ML models, including Auto ML. Students may receive credit for only one of the following courses: ARIN 440 or DATA 440.
Data Ethics
ARIN 450 | 3 Credits
Course Desc: Prerequisite: DATA 430. A study of ethics within the context of data science, machine learning, and artificial intelligence. Emphasis is on examining data and model bias; building explainable, fair, trustable, and accurate predictive modeling systems; and reporting responsible results. Topics include the technology implications of human-centered machine learning and artificial intelligence on decision-making in organizations and government and the broader impact on society, including multinational and global effects. Students may receive credit for only one of the following courses: ARIN 450 or DATA 450.
Artificial Intelligence Solutions
ARIN 460 | 3 Credits
Course Desc: (Designed to help prepare for the AWS Certified Machine Learning or Microsoft Designing and Implementing an Azure AI Solution exam.) Prerequisite: DATA 430. A hands-on, project-based study of artificial intelligence and machine learning solutions to complex problems. Topics include natural language processing, computer vision, and speech recognition. Students may receive credit for only one of the following courses: ARIN 460 or DATA 460.
Advanced AI Developer Topics
ARIN 470 | 3 Credits
Course Desc: Prerequisites: 33 credits of major coursework. A hands-on project-based study of concepts, tools, and techniques relevant to AI developers. Topics are selected to reflect the latest trends in artificial intelligence.
Advanced AI Applications Topics
ARIN 475 | 3 Credits
Course Desc: Prerequisites: 33 credits of major coursework. A hands-on project-based study of concepts, tools, and techniques relevant to the use of AI applications. Topics are selected to reflect the latest trends in artificial intelligence.
Workplace Learning in Artificial Intellegence
ARIN 486A | 3 Credits
Course Desc: Prerequisites: 9 credits in the discipline and prior program approval (requirements detailed online at www.umgc.edu/wkpl). The integration of discipline-specific knowledge with new experiences in the work environment. Tasks include completing a series of academic assignments that parallel work experiences.
Workplace Learning in Artificial Intelligence
ARIN 486B | 6 Credits
Course Desc: Prerequisites: 9 credits in the discipline and prior program approval (requirements detailed online at www.umgc.edu/wkpl). The integration of discipline-specific knowledge with new experiences in the work environment. Tasks include completing a series of academic assignments that parallel work experiences.
Artificial Intelligence Capstone
ARIN 495 | 3 Credits
Course Desc: Prerequisites: 36 credits of major coursework. A project-based, practical application of the knowledge, technical skills, and critical thinking skills acquired during previous study designed to showcase the student's expertise in artificial intelligence. Individually selected projects involve either a focus on AI applications or AI development and result in a peer-reviewed final deliverable and presentation. Topics are selected from student-affiliated organizations or employers, special government/private agency requests, or other faculty-approved sources in a wide range of domains, such as healthcare, financial services, marketing, sciences, and government.
Introduction to Artificial Intelligence
ARIN 610 | 3 Credits
Course Desc: A comprehensive introduction to the foundational principles, terminology, and concepts of artificial intelligence (AI). Designed for students from diverse backgrounds with emphasis on the knowledge and skills needed to understand, evaluate, and collaborate effectively on AI initiatives within an organization. Topics include machine learning, deep learning, foundation models, and generative AI as well as their applications in different domain areas.
AI Ethics
ARIN 615 | 3 Credits
Course Desc: (Formerly DATA 615) An overview of current ethical issues in artificial intelligence (AI) and data science arising throughout the analytics life cycle. The goal is to create ethically driven and responsible AI solutions that enhance human problem-solving and decision-making, identify the sources of bias and discrimination in machine learning, and build models that promote trust in data. Topics include established and emerging guiding principles for AI ethics, such as explainability, fairness, robustness, transparency, accountability, inclusiveness, and privacy. Students may receive credit for only one of the following courses: DATA 615 or ARIN 615.
Contemporary Topics in AI
ARIN 620 | 3 Credits
Course Desc: A project-based study of cutting-edge concepts, tools, and techniques in the rapidly evolving field of artificial intelligence. Designed to keep pace with current developments, explores a curated set of contemporary topics that reflect the latest trends and innovations in AI. Emphasis is on hands-on projects for real-world applications, responsible development, and interdisciplinary use cases across industries. Topics include generative AI, foundation models, multimodal learning, AI ethics and governance, autonomous systems, edge AI, and advancements in natural language processing and computer vision.
Deep Learning and Neural Networks
ARIN 655 | 3 Credits
Course Desc: (Formerly DATA 655) Prerequisite: DATA 645. A practical exploration of the fundamental concepts, architectures, and applications of deep learning in the field of artificial intelligence. The goal is to develop deep learning models and apply them to solve real-world problems in a wide range of domains, such as healthcare, finance, marketing, and cybersecurity. Topics include backpropagation, convolutional networks, recurrent networks, and generative adversarial networks, and their applications. Students may receive credit for only one of the following courses: DATA 655 or ARIN 655.
Computer Vision and Natural Language Processing
ARIN 665 | 3 Credits
Course Desc: (Formerly DATA 665) Prerequisite or corequisite: ARIN 655. A comprehensive overview of artificial intelligence with a specific focus on Natural Language Processing (NLP), Computer Vision, Recommender Systems, and Anomaly Detection. The aim is to develop AI applications relevant to real-world scenarios in multiple disciplines and domains. Topics include text and images classification, sentiment analysis, natural language and image generation, and content-based filtering. Discussions explore fraud detection, network intrusion detection, and system health monitoring. Students may receive credit for only one of the following courses: DATA 665 or ARIN 665.
AI-Cyber Capstone
ARIN 690B | 3 Credits
Course Desc: Prerequisites: ARIN 610, ARIN 615, ARIN 620 and CYOP 655. A comprehensive, project-driven exploration of artificial intelligence applications in cybersecurity. Students will integrate knowledge from across the specialization to address real-world challenges, demonstrating their ability to apply AI techniques to enhance the security of information systems and to safeguard AI-driven systems themselves. Emphasis is placed on practical implementation, critical thinking, and aligning solutions with organizational security needs and objectives.