Read the book here.
An interactive introduction to predictive modelling and machine learning for biomedical researchers, developed through a single running example: predicting age from cortical structure in the IXI dataset.
Every practice page is a Jupyter notebook and can be run in the cloud from the book itself (the rocket icon at the top of each page opens it in Binder or Colab), or locally:
git clone https://github.com/pni-lab/predmod_lecture.git
cd predmod_lecture
pip install -r requirements.txt
jupyter lab contents/pip install -r requirements.txt
jupyter-book build contents/The result is written to contents/_build/html/. Notebook outputs are committed to the
repository, so the build itself does not execute anything. The notebooks are re-executed in
continuous integration (.github/workflows/book.yml), which is what keeps those committed outputs
trustworthy: if a notebook stops running, the build fails, and if its outputs drift from what is
committed, the run reports it.
To re-execute everything locally:
jupyter nbconvert --to notebook --execute --inplace contents/*/*.ipynb| path | contents |
|---|---|
contents/ |
the book: one directory per chapter, plus _toc.yml and _config.yml |
ex_data/IXI/ |
the example dataset (cortical volumes and age, n=638) |
figures/ |
scripts that generate the static figures used in the text |
The example data is derived from the IXI dataset,
processed with FreeSurfer to give volumes of the 68
Desikan-Killiany cortical regions {cite:p}desikan2006automated. IXI is made available under
CC BY-SA 3.0; please acknowledge the source if
you use it.
Corrections and suggestions are very welcome — please open an issue.
The book is licensed under CC BY-SA 4.0, matching the share-alike terms of the IXI data it builds on. Code samples may additionally be used under the MIT license.