[Note: over the past decade I have taught a variety of courses primarily with a focus on fintech.  Recently I was given the opportunity to merge two areas of interest: teaching about AI-related topics and the financial services industry. In addition to slides, below is a long form set of examples and exercises of the blended topic assisted via agentic deep research (guess which parts!).  This is intended for a classroom setting, feel free to re-use.]

Section 1: Introduction – Key Concepts and Definitions

Artificial Intelligence is a broad field of computer science focused on creating systems that perform tasks normally requiring human intelligence. Think of AI as an umbrella term for technologies enabling machines to learn, reason, and make decisions. For example, an AI chess program can evaluate millions of moves to play at a grandmaster level. Machine Learning (ML) is a subset of AI involving algorithms that improve through experience – in other words, ML systems learn from data to get better at a task without being explicitly programmed for every scenario (AI vs. ML vs. Generative AI: Basics). If AI is the overarching concept of smart machines, ML is one of the key techniques allowing those machines to learn. Generative AI is another subset of AI that focuses on creating new content. Rather than just analyzing existing data, generative models can produce text, images, or other media that mimic human-created content. In plain terms, Generative AI (GenAI) models like GPT-4 or DALL-E don’t just answer questions – they generate brand-new essays, code, visuals, and more, based on patterns learned from vast training data.

… we are now Shifting gears from agentic content.