Technological progress allows data collection at an ever-increasing rate. This is true for many scientific fields including neuroscience where structural and functional brain-imaging data is now available for tens of thousands of subjects. This raises the question of how these large amounts of data can be efficiently and effectively transformed into insights to improve our understanding of the brain as well as foresight that can influence clinical decisions. Machine-learning-based analytics provides a plethora of tools to tackle such questions. Importantly, machine-learning can provide predictions at the individual level which can help understand individual differences, as opposed to the traditionally used group analyses. Machine-learning is a vast toolbox that offers a multitude of options while setting up a prediction pipeline, including choices for data representation and learning algorithms. The success of a machine-learning pipeline critically depends on making proper choices.

Resources used
https://www.fz-juelich.de/inm/inm-7/EN/Forschung/Applied%20Machine%20Learning/_node.html