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Passt

EN: Passt: An Introduction to AI

DE: Passt: KI-Grundlagen

A twelve-week introduction to machine learning for people who will have to live with these systems, argue with them, buy them, and explain them to somebody in a meeting who has not read anything.

Passt is German for "it fits". It is also what you say when something is good enough. A model that fits its data is doing its job, and a model that fits its data too well is lying to you, and the whole course is about telling those two apart.

Live at https://i-yam.github.io/passt/


What is here

index.html            course homepage, bilingual
modules/              42 self-contained mini-modules, one folder each
notebooks/            Colab tinker sessions
assets/               shared theme tokens and the i18n contract

Every module is a single self-contained HTML file. No build step, no bundler, no framework. Open one in a browser and it works. The only external dependency is the KaTeX CDN, and only on pages with formulas.

The twelve classes

# Class Modules
1 What Is Intelligence? What Is Knowledge? can_machines_think spiders_and_legs is_ai quiz1
2 Learning Has Many Faces duction anscombe parametric code_comprehension
3 Linear Regression hot_or_cold sgd linregtutorial
4 Local Models and Geometry glocal neighbours bv dimcurse
5 Regularisation regularisation ridge lasso + Colab
6 Classification Fundamentals decisions shrooms bayes test6
7 Decision Trees akinator entropy titanic
8 Ensembles ensambles boosting
9 Validation and Model Selection testtrain folds simpson
10 Recommendations and Neural Networks people_like_you boards_of_destiny game_of_life nets
11 LLMs and Generative AI gaps apophenic prompts
12 AI in Practice persona same_cv magenta_line
Exam exam open

Bilingual

Every page carries a language toggle in the top right. English renders first, German sits behind the toggle, and the button always shows the other language.

The German is not a translation of the English, it is written to be read. Target reader is B2 in either language.

Publishing

GitHub Pages from the repository root. .nojekyll is present so directories starting with an underscore are not swallowed. Nothing else is required.

Two things worth not smoothing out

Some modules correct claims that this course itself used to make. dimcurse carries a note that an earlier version stated a probability that was wrong by a factor of two. That is deliberate. A course built on falsifiability that never catches itself is not making the argument it thinks it is. If you find more mistakes, open issues, we'll fix them and the "scars" will make the course even better.

Sections about people who died, were wrongly convicted or wrongly arrested carry no jokes in either language. bayes on Sally Clark, magenta_line on Air France 447, and titanic and entropy on the sinking are written plain on purpose.

Licence

Content CC BY-SA 4.0. Code MIT. See LICENSE.

About

This is a collection of vibe-coded materials for an introductory course on artificial intelligence and machine learning.

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