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Weeks

Week 1

Neuro-Data Science Bootcamp at McGill University

Week 1 will introduce participants to a reproducible computational toolkit for neural data science, 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. 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 100% of the final grade for the course QLSC612, and 10% of the final note for the project-based courses (PSY6983 or COMP490/COMP6971).

View the schedule for that week

Week 2

Project definition at CRIUGM - University of Montreal

CRIUGM building

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 be presented orally and in writing, with rounds of feedback, and revised by the end of week 2. This project description will count for 10% of the final grade (PSY6983 or COMP490/COMP6971).

Week 3

Project implementation at Concordia University

During week 3, participants will work on their project. The content of a typical day will include:

  • Work on projects. Most of the time will be reserved to actually doing the work.
  • Project clinics. Get daily feedback and support from instructors and residents.
  • Tutorials. Tutorials will be organized on demand.
  • Collaborate. Take time each day to help someone else with their project.

By the end of week 3, participants will make an oral presentation on a data visualization related to their project, and identify their objectives for the final week. This presentation will count for 10% of the final grade (PSY6983 or COMP490/COMP6971).

Concordia building

Week 4

Project wrap-up and communication at Montreal Polytechnique

Polytechnique building
During week 4, participants will concentrate on finalizing project results and producing deliverables. The daily structure will be similar to week 3. Participants will have to produce a written deliverable for their project, which will be published on this website. These deliverables will have to be submitted by June 12th (one week after the end of the school), and will count for 30% of the final grade (PSY6983 or COMP490/COMP6971). At the end of week 4, participants will make a short oral presentation on their project, which will count for 28% of the final grade (PSY6983 or COMP490/COMP6971). Finally, there will be a participation grade for the full 4 weeks, which will count for 12% of the final grade (PSY6983 or COMP490/COMP6971).