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:
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).