ML Course — Environment Setup

This repository is the starting point for the ML Seminar at FU Berlin (Bachelor). It provides a reproducible Python environment and ready-to-use templates so you can focus on the course content rather than setup.

Repository structure

ml-setup/
├── pyproject.toml       # Python dependencies
├── quarto_intro/        # Quarto templates (report, slides) and citation example
│   ├── report.qmd       # Report rendered to HTML and PDF
│   ├── slides_revealjs.qmd   # Browser-based slides (RevealJS)
│   ├── slides_beamer.qmd     # PDF slides (LaTeX Beamer)
│   └── references.bib   # BibTeX reference file
└── jupyter_demo/        # Minimal Jupyter notebook demo
    └── demo.ipynb

The environment is managed with uv and pinned in uv.lock to ensure everyone runs the exact same package versions.

Prerequisites

Install uv (fast Python package manager):

# macOS / Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows (PowerShell)
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

Installation

1. Click "Use this template" → "Create a new repository" on GitHub to create your own copy.
2. Clone your new repository and install the environment:

git clone https://github.com/<your-username>/<your-repo-name>.git
cd <your-repo-name>
uv sync

This will automatically:

• Download and install Python 3.13 (if not already available)
• Create a virtual environment in .venv/
• Install all pinned dependencies from uv.lock

Running Jupyter

uv run jupyter lab

Initializing the Jupyter Kernel

If Jupyter does not automatically detect the environment, register it manually as a kernel:

uv run python -m ipykernel install --user --name ml-setup --display-name "ML Course"

Then restart Jupyter Lab and select ML Course from the kernel menu.

Packages included

Package	Purpose
numpy	Numerical computing
pandas	Data manipulation
scikit-learn	Machine learning
matplotlib	Plotting
torch	Deep learning (PyTorch)
statsmodels	Statistical models
jupyter / ipykernel	Notebook environment

Updating the environment

If the course materials are updated and you need to sync the latest dependencies:

git pull
uv sync