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Setting up your computer
How to handle Jupyter Notebooks
Overview central steps
Look at the big picture
Get the data

Create a test data set

Explore and Visualize

Prepare the data

Overview Training and Tuning

Linear Regression

Polynomial Regression

Regularized Linear Regression

Random Forests Regression

Support Vector Regression

Boosting (Regression)

Exercise on regression

Present and store your model

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Introduction

K-Means

Gaussian Mixtures

DBSCAN and HDBSCAN

Exercise

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Plotting Basics

Introduction to Seaborn

Plotting Distributions with Seaborn

Multivariate views

BiA-Bob

AI-assisted Notebook generation

Translating notebooks

Statistical testing

Under the hood: Configure bia-bob’s behaviour through system messages

AI School Website

License CC-BY 4.0

Imprint

Site Navigation

Setting up your computer
How to handle Jupyter Notebooks
Overview central steps
Look at the big picture
Get the data

Create a test data set

Explore and Visualize

Prepare the data

Overview Training and Tuning

Linear Regression

Polynomial Regression

Regularized Linear Regression

Random Forests Regression

Support Vector Regression

Boosting (Regression)

Exercise on regression

Present and store your model

placeholder

Introduction

K-Means

Gaussian Mixtures

DBSCAN and HDBSCAN

Exercise

placeholder

Plotting Basics

Introduction to Seaborn

Plotting Distributions with Seaborn

Multivariate views

BiA-Bob

AI-assisted Notebook generation

Translating notebooks

Statistical testing

Under the hood: Configure bia-bob’s behaviour through system messages

AI School Website

License CC-BY 4.0

Imprint

Ctrl+K

Leibniz MMS AI School 2025

Setup

Setting up your computer
- Prepare your Jupyter Lab environment

Python Jupyter Notebooks

How to handle Jupyter Notebooks

Introduction to Machine Learning / Regression

Overview central steps
Look at the big picture
Get the data
Create a test data set
Explore and Visualize
Prepare the data
Overview Training and Tuning
Linear Regression
Polynomial Regression
Regularized Linear Regression
Random Forests Regression
Support Vector Regression
Boosting (Regression)
Exercise on regression
Present and store your model

Data Wrangling

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Machine Learning Techniques / Classification

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Machine Learning Techniques / Clustering

Introduction
K-Means
Gaussian Mixtures
DBSCAN and HDBSCAN
Exercise

Deep Learning

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Data Visualization

Plotting Basics
Introduction to Seaborn
Plotting Distributions with Seaborn
Multivariate views

Large Language Models

BiA-Bob
AI-assisted Notebook generation
Translating notebooks
Statistical testing
Under the hood: Configure bia-bob’s behaviour through system messages

Links

AI School Website
License CC-BY 4.0
Imprint

repository
open issue

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By Stefan Zahn, Martin Rudolph, Matthias Täschner, Robert Haase

Last updated on 2025-09-27.

Copyright: Licensed CC-BY 4.0 unless mentioned otherwise. Contributions and feedback are welcome.