Overfit & Chill

Twelve classes. No formulas on the first day. The math arrives when you need it.

This course is for people who work with data, commission data products, or manage teams that build them. You will not leave knowing how to train the next language model. You will leave knowing what questions to ask when someone shows you one.

Every topic has a direct track and an optional deep dive for the curious. The tags tell you how long something takes and how much code is involved.

12 classes 3 h each + homework EN / DE

Classes

01 What Is Intelligence? What Is Knowledge? Before any algorithm, two questions. ›

Before any algorithm, two questions. What are we trying to build? And how do we know if we know anything?

mini-module ~20 min no-code try-without-ai

Can Machines Think?

Turing asked this in 1950. The question is still open. ML taxonomy, weak vs strong AI, and an analogy game that forces you to take a position before the theory arrives.

mini-module no-code try-without-ai statistics

Empiricism and the Logic of Science

Sextus Empiricus, Karl Popper, and the question of what counts as knowledge. If it cannot be falsified, it is not science. This is the epistemological frame the rest of the course runs on.

group discussion ~30 min no-code try-without-ai

What Is Intelligence?

Small groups produce definitions, merge, and critique each other. The class collects a capability list on the board, then sorts each item: weak AI, strong AI, or neither.

In-class activity — no link

intermediate ~10 min no-code try-without-ai

Is This AI?

Calculator, spam filter, GPS, Face ID. Vote on each one. The answer is not always obvious, and that is the point.

intermediate ~10 min no-code try-without-ai

Class 1 Quiz

A short knowledge check on the concepts from Class 1.

Homework: Find one dubious AI claim in the media, advertising, or a news headline. Bring it to Class 2 with your diagnosis.

02 How Machines Learn From human knowledge to machine knowledge. ›

03 Linear Regression + First Encounter with Lies in Data The simplest useful model. ›

04 Regularization & Feature Engineering When the model fits too well. ›

05 Classification Fundamentals When the answer is a category, not a number. ›

06 Local Models & Geometry Not every question has one global answer. ›

07 Decision Trees A model you can draw on a whiteboard. ›

08 Ensembles One tree overfits. A thousand trees generalize. ›

09 Validation & Model Selection You built a model. Now prove it works. ›

10 Recommendations & Neural Networks How machines learn what you want. ›

11 LLMs & Generative AI What changed. ›

12 AI in Practice — Bias, Society, Synthesis What happens when the model meets the real world. ›