ml-unsupervised

Introduction

This repository contains the materials for the course on applied unsupervised machine learning.

Course Developers: see our guidelines page if contributing materials.

These materials are released under a CC BY 4.0 license.

Steps for course developers

• Create a Python virtual environment

   python3 -m venv .venv

source .venv/bin/activate

• Install all requirements

   pip install -r requirements.txt

• Install Quarto and VS Code

• Change the .qmd files

• Render using quarto

quarto render

• Commit files in _freeze folder and any other .qmd files changed

chmod 755 gitshell.sh
./gitshell.sh

• A single command to compile the materials

./compile.sh

• Push changes to the repository

git commit -m "Updated materials"
git push

Directory structure

• README.md: Project overview and setup instructions
• requirements.txt: Python dependencies
• references.bib: Bibliography for the course
• course_files/scripts/: Python scripts for data generation and examples
• course_files/data/: Example datasets
• materials/: Course content and chapters in Quarto format
• .venv/: (Optional) Virtual environment for Python dependencies

Contact

Soumya Banerjee

sb2333@cam.ac.uk