📊 AI4AS - EAAP 2026: Summer school "Pitfalls in machine learning"

Jupyter notebooks on Pitfalls in Machine Learning in Animal Science.

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This repository contains the code, exercises, and solution notebooks for the AI4AS Summer School Pitfalls in Machine Learning.

The slides can be found in: docs/AI4AS pitfalls in machine learning.pdf

The solution notebooks are in notebooks_code/:

• class_01_solution.ipynb
• class_02_solution.ipynb
• class_03_solution.ipynb

📁 Project Structure

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├── data/
│   ├── raw/            # Original raw datasets
│   └── processed/      # Data processed by scripts and/or notebooks and saved afterwards
├── notebooks_code/     # Solution notebooks
├── docs/               # Slides
├── README.md           # Project overview

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⚙️ Getting Started

📦 Clone and Initialize the Environment

1. First, fork this repository to your own GitHub account so you that you have your own copy:

• Open the repository on GitHub.
• Click Fork (top-right) and create your own copy.

2. This project includes a .devcontainer and requirements.txt, the easiest way to start is with GitHub Codespaces and code directly in a web browser with VSCode without any local installation. A Codespace acts as a remote development environment in the cloud. Alternatively, you can clone the forked repository locally and set up a virtual environment to install the required packages (in VS Code, open the Command Palette and select Python: Create Environment).

To get started with the github codespace, you can follow the next steps:

• Open your forked repository on GitHub.

  • Click Code → Codespaces (see Figure 1). For best performance and to avoid kernel issues, select the largest available machine type (see "+...").
  Figure 1. Creating a new Codespace from the repository Code menu.

• Wait until the container finishes building (this may take a few minutes the first time).

• Open the first Jupyter notebook and select the kernel (Python 3.12) (see Figure 2).

  Figure 2. Selecting the kernel in a Codespace.

• Always Save/sync your work by committing and pushing changes to your fork (otherwise your work in Codespaces can be lost if the environment is stopped or deleted):

  1. After each exercise, save your notebook (Ctrl+S / Cmd+S).
  2. Open the Source Control tab in VS Code (branch icon).
  3. Review changed files and write a short commit message, for example: Exercise 1 finished.
  4. Click Commit.
  5. Click Sync Changes (or Push) to upload your work to your GitHub fork.

  Tip: Commit and push regularly (e.g., every 15-30 minutes) to avoid losing progress.

If you would like to inspect datasets visually during the course, you can install the Data Wrangler extension from the Extensions panel (left sidebar). After installation, a View Data button will appear at the top.