The 9 credit-hour certificate can be completed in nine months or less, though students can move through the content at their own pace. Courses are 8 weeks each fall and spring and summer. Courses are strategically designed to enhance the instruction and interaction between industry and academic leaders.
Plan of Study Overview
REQUIRED CORE COURSES (6 CREDITS)
| GRAD 50200 | 1 credit | Interdisciplinary AI Fundamentals: Bridging Knowledge |
| GRAD 50400 | 2 credits | Advanced AI Fundamentals for Technical Professional |
| CNIT 57200 | 3 credits | AI Applications in Cybersecurity |
ELECTIVES (Select One):
| CNIT 57300 | 3 credits | Adversarial Techniques in AI |
| CNIT 57400 | 3 credits | Information Security and Privacy in the Age of Large Language Models |
Course Descriptions
GRAD 50200: Interdisciplinary AI Fundamentals: Bridging Knowledge: This course provides foundational concepts for AI for those in both MSAI majors. This course covers the history of AI, the foundations of artificial reasoning and knowledge, and practical skills for communicating about AI technology, projects, and workflow. This graduate course, designed to be accessible to learners with diverse backgrounds, serves as a foundational introduction to the field of AI. Whether you come from a technical or non-technical background, this course will provide you with a solid grasp of AI concepts, principles, and their real-world applications. To be successful in this course, we encourage students to know math (e.g., algebra and calculus) and a basic understanding of programming (e.g., R, Python, or Java).
GRAD 50400: Advanced AI Fundamentals for Technical Professional: Artificial Intelligence (AI) is the driving force behind the transformation of industries, research, and technology. For students with a highly technical background, this 2-credit hour graduate course offers a deep dive into the fundamental principles, theories, and applications of AI. Specifically, this course will introduce students to the field of data mining and machine learning, which sits at the interface between statistics and computer science. Data mining and machine learning focus on developing algorithms to automatically discover patterns and learn models of large datasets. This course introduces students to the process and main techniques in data mining and machine learning, including exploratory data analysis, predictive modeling, descriptive modeling, and evaluation. To be successful in this course students should have experience with programming such as Python and R and have a background that includes calculus, linear algebra, algorithms, and probability theory.
CNIT 57200: AI Applications in Cybersecurity: This course introduces students to the intersection of Artificial Intelligence (AI) and cybersecurity, providing an in-depth understanding of how AI technologies are revolutionizing the field. Topics include the application of machine learning (ML), natural language processing (NLP), and deep learning to enhance cybersecurity practices, including threat detection, incident response, and vulnerability management.
CNIT 57300: Adversarial Techniques in AI: This course provides an exploration of adversarial techniques in artificial intelligence (AI) and machine learning (ML). Students will learn about various attack methodologies, including data poisoning, evasion attacks, backdoor attacks, and adversarial perturbations. The course will also cover defense mechanisms and countermeasures to ensure robust AI systems. The course emphasizes hands-on experience, with students engaging in case studies, coding assignments, and project-based learning to understand the intricacies of adversarial machine learning.
CNIT 57400: Information Security and Privacy in the Age of Large Language Models: This course will serve as an introduction to natural language processing (NLP) with the emphasis in and applications on information assurance, security, and privacy. The topics will review the state of the art of NLP with the focus on natural language text and information received from and implied in it. The course will cover a wide range of techniques and applications of NLP to security and privacy, with identifiable advantages and disadvantages.
Note:
- Must receive a B- or better in each of the three courses.
- Courses are subject to change due to faculty discretion.
- The program follows a defined course sequence and structure. Within this sequence, GRAD 50200 – Interdisciplinary AI Fundamentals: Bridging Knowledge and GRAD 50400 – Advanced AI Fundamentals for Technical Professionals are integral components of the curriculum. Students may waive these requirements by demonstrating prior mastery of the subject matter through approved evidence or by achieving a satisfactory score on a pre-examination.