Week 1 will introduce participants to a computational reproducibility toolkit, as well as a basic grounding in supervised and unsupervised machine learning methods. Short lectures and hands-on tutorials throughout the five days will provide participants with familiarity applying these methods to real data. Dr Félix Antoine-Fortin (Calcul Québec) and Dr Manjari Narayan (Stanford University) will be residents during week 1. As a participant, at the end of Week 1 you should be able to answer questions such as:

• What is version control, and how can I use it to improve my workflow?
• Which data standards can be used to organize neuroimaging data, and why should I adopt them?
• How should I visualize and define features for machine learning in neuroimaging?
• What are the basic principles underlying deep learning, and how do they differ from classical machine learning?

A short quizz will be organized at the end of week 1, to check that participants have integrated the key points of the week. This quizz will count for 10% of the final note.

Week 2 will feature “pitch sessions”, where instructors give a short overview of skills or resources that can be incorporated into projects. Week 2 will be mostly focused on defining and piloting the project. As a participant, you will need to decide:

• What general topic do you want to work on? e.g. group comparison using fMRI, software for analysis of MEG data
• What skills do you want to learn, working on this project? e.g. preprocess fMRI data and run a classifier with sklearn, how to use git, etc.
• What resources do you want to work on? e.g. the CORR dataset, the nipype library, the Glasser parcellation paper, etc.
• What objectives do you want to achieve with the project? e.g. find differences in connectivity between two groups, replicate a multimodal brain parcellation, etc.
• What will be the outcome(s) of your project? a short proceedings paper, a new public dataset, a new feature in a toolbox, etc.

Each project will go through one round of written submission and feedback, and revised by the end of week 2. This project description will count for 10% of the final note.

During week 3, participants will work on their project. Dr Joana Pereira (Karolinska Institute) will be resident for the week. The content of a typical day will include:

• Work on projects. Most of the time will be reserved to actually doing the work.
• Tutorials. Tutorials will be organized on demand.
• Collaborate. There will be time each day for participants to help someone else with their project

By the end of week 3, participants will prepare a short video summarizing their project. This video will count for 10% of the final note.

Week 4 will concentrate on finalizing project results and producing deliverables. Dr Pamela Douglas (University of California, Los Angeles), will be the resident for the week. Participants will have to produce a written deliverable for their project, such as a short BrainHack proceeding paper or a blog post. These deliverables will have to be submitted by September 6th (one week after the end of the school), and will count for 30% of the final grade. During week 4, participants will also make a short oral presentation of their project, which will count for 28% of the final grade. Finally, there will be a participation grade for the full 4 weeks, which will count for 12% of the final grade.