CYBER 510: How AI Works provides a clear, structured introduction to artificial intelligence for professionals who need to understand AI systems without becoming engineers or data scientists. Rather than focusing on tools or coding, the course explains how modern AI actually functions under the hood, why different approaches emerged, and what their capabilities and limits are. The goal is to replace buzzwords with durable conceptual understanding that professionals can rely on when advising, managing risk, drafting policy, or making strategic decisions involving AI.
The course begins with a high-level overview of artificial intelligence and its core ideas, followed by a concise history that explains how early symbolic approaches gave way to data-driven methods. Students then explore “old-school” machine learning techniques to understand how algorithms learn from data, why feature selection matters, and where traditional models succeed or fail. Building on that foundation, the course introduces neural networks, explaining how layered representations enable more complex learning and why this shift transformed AI performance.
The later modules focus on deep learning architectures that dominate modern AI systems. Students examine convolutional neural networks to understand how machines interpret images and spatial data, and conclude with generative AI, including large language models, to see how contemporary systems produce text, images, and other outputs that appear creative or human-like. Throughout the course, technical concepts are explained in plain language, reinforced with diagrams and examples, and connected to real-world uses and risks.
By the end of CYBER 510, students will understand how different AI approaches work, why they behave the way they do, and how to ask informed questions about reliability, bias, transparency, and appropriate use. This course equips professionals to engage confidently with AI developers, vendors, regulators, and stakeholders—and to be the knowledgeable person in the room when AI decisions matter.
