As I continue teaching data science and work with statistics students, it becomes more clear that we in the data community are not reaching the rest of the world, and often not the rest of the programming community, in the precise definition of a model. In this talk, I attempt to shine some light into this black box.

Starting with classical statistics and the linear regression, I explain in detail what a basic model is, in a way that is accessible to those without a technical technical background. Building on this concept, I move to basic machine learning, including how we select and train models. The topic of generative AI, specifically LLMs, rounds out the talk.

At every level, I talk about the capabilities and limitations of our models. And, rather than remaining theoretical, we find practical applications from economics, logistics, and, naturally, an LLM. None of these things are magic. So let’s follow my favorite economics professor and try to turn this black box into a grey box!