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AI4Good Lab - Tuition-free Machine Learning Bootcamp - Deadline extended

[This opportunity is not affiliated with the Department, Faculty of Arts & Science, or University of Toronto. Registration fees may apply. We share this opportunity at the request of the organizers in case it is of interest to our student body. Participation is as the sole discretion of the individual.]

The AI4Good Lab is a tuition-free machine learning bootcamp for women-identified STEM students and graduates across Canada.

The program consists of a mashup of a machine learning bootcamp, hackathon, and self-directed team projects. It was founded by Doina Precup and Angelique Manella in Montreal in 2017 and is founded by the OSMO foundation and CIFAR.

Our call for applications is open now until February 29th at 11:59 PM and we are seeking applicants from around the country!

To apply: www.ai4goodlab.com/apply

Program details

  • Montreal program, hosted by Mila: May 4th to June 19th

      - Four weeks of Machine Learning curriculum
    
      - Two day hackathon (collaboration with Google)
    
      - Three weeks of self-directed team projects to develop 
        an AI for social good prototype
    
  • Edmonton pilot program, hosted by Amii: exact dates to be announced

      - Machine learning curriculum
    
      - Hackathon
    
  • We charge no tuition fees (small $75 registration fee for selected applicants only)

  • Every participant receives a small stipend (location dependent)

  • Travel grants available to reduce economic barriers

Eligibility criteria

  • Woman-identified

  • Located across Canada

  • Currently enrolled in a STEM focused undergraduate program or have graduated within the last 2 years from such a program

  • Strong math background, some programming experience

  • Interested in how AI can be used as a tool for social change

More details are on our website (www.ai4goodlab.com). Questions can be sent to info@ai4goodlab.com

  [General boards] [Winter 2023 courses] [Fall 2022 courses] [Summer 2022 courses] [Older or newer terms]