sklearn and nilearn

Team: Alexa Pichet-Binette, Alexandre Hutton, Désirée Lussier, Gaël Varoquaux

Date: November 14th, 2019, 9h-17h. Breakfast/registration at 8h30.

Summary: This course will be a hands-on/type-along introduction to machine learning for neuroimaging problems with scikit-learn and nilearn.

Morning (9h-12h30): introduction to machine-learning with scikit-learn

This part of the course will follow the scikit-learn chapter of the scipy-lectures, found here. This includes:

  • Basic principles

  • Supervised learning: classification, the example of handwritten digits

  • Supervised learning: regression, the example of housing data

  • Measuring prediction performance

  • Unsupervised learning: dimension reduction and visualization

  • Chaining estimators: the example of eigenfaces

  • Parameter selection, validation, and testing

Afternoon (13h30-17h): introduction to nilearn

This part of the course will provide a general introduction to nilearn, including a full prediction pipeline with fMRI data.


  • Basic familiarity with Python would be preferable

  • You will need enough space for Anaconda and all the course data (~4GB).

Installation instructions

Please join the brainhack mattermost and the channel main-training-ml. All the notebook tutorials, as well as installation instructions are available on the main-training-ml github repository.