## 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](http://www.scipy-lectures.org/packages/scikit-learn/index.html). 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.

**Prerequisites**

- 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](https://mattermost.brainhack.org) and the channel `main-training-ml`. All the notebook tutorials, as well as installation instructions are available on the [main-training-ml](https://github.com/main-training/main-training-nilearn-ml) github repository.