Making an AI/ML presentation for dummies

I work as a data scientist at a non-tech company, and I was asked to do a 1h knowledge sharing session on AI/ML for an audience that doesn't really have a STEM background.

Of course, I'll only be able to scratch the very surface of it, but I'd like to cover important concepts, e.g. supervised vs unsupervised learning, train-tune-test split, over/underfitting, curse of dimensionality, data leakage and other common pitfalls, xAI and so on. Maybe have bayesian inference and/or linear …

anchor audience bayesian bayesian inference concrete course data data leakage datascience data scientist dimensionality examples inference knowledge linear linear regression making presentation regression session stem tech test underfitting unsupervised unsupervised learning work xai