What it does
This repository holds the code behind the figures in two well-known textbooks: Probabilistic Machine Learning: An Introduction, and its follow-up on advanced topics.
The code is in notebooks, documents that mix explanation and runnable code. They use standard Python tools, plus JAX, TensorFlow and PyTorch in places.
You can run them in your browser with Google Colab or on your own computer. The README says the code has been in maintenance mode since September 2022.
Who it's for
Students and self-learners working through the books who want to see and change the code behind each figure.
Can I use it without coding?
Developers only. You need to be comfortable running Python notebooks. New to this? Start here.
Can I run it?
- Cost
- FreeNothing to pay or sign up for.
- Runs on
- Web browser
- Computer
- An ordinary computer is fine
- API key
- Not needed
Try it
- Open the notebook collection from the project page and pick a chapter.
- Run a notebook in Google Colab in your browser, which has most of the tools already installed.
Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?
Before you install
- Only download from the official GitHub page. Copies with similar names exist for many popular projects.
- License (MIT): Very permissive: free to use, change and share, including for business.
- More on staying safe