Full-time summer AI bootcamp.

Machine learning summer training program for women and gender diverse people across Canada.

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The AI4Good Lab is a 7-week program that equips women and gender diverse people with the skills to build their own machine learning projects. We emphasize mentorship and curiosity-driven learning to prepare our trainees for a career in AI.

Next program May 6 - June 26, 2024

Build the fundamentals of your career in AI.

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Study machine learning.

Dive into the fundamentals of machine learning to see problems in different ways, answer important questions, and inspire confidence in your new skills.

A group of students from the 2023 cohort

Build enduring relationships.

Explore the world of AI with our speakers and lecturers, and refine your interests with guidance from your mentors and teaching assistants.

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Develop projects with a purpose.

Reinforce your new skills with team-driven machine learning projects. Raise each other up and collaborate on solutions that drive social progress.

Students from the 2023 cohort present their prototype

Share your voice.

AI thrives from a diversity of perspectives. Bring yours to our supportive network of and gender diverse people who are changing the culture in technology and shaping the future of AI as a tool for social good.

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How you will learn.

Weeks 1 — 4 ML Bootcamp.

Learn.

Gain a technical introduction and a big-picture overview of AI from our lectures and talks. Our lectures cover neural networks, CNNs, RNNs, natural language processing, and reinforcement learning. Our talks contextualize the lectures by showcasing real-life applications of AI and discussing pressing ethical challenges.

Network.

Network with representatives from our sponsors and explore the opportunities available to you in the industry. The connections you make with peers and professionals alike are designed to serve you throughout your career!

Practice.

Solve hands-on exercises, put your new skills into action at our mini data-processing hackathon, and have all your questions answered by your graduate-level TAs.

Weeks 5 — 7 Team Projects.

Build.

Cement your newly acquired knowledge in AI by working in teams to develop a machine learning prototype, from start to finish.

Mentorship.

Enhance your projects with guidance from your mentors who join us from our partners in the AI industry.

For Good.

Use your project as an opportunity to explore solutions to real-world problems and evolve your understanding of ethical design principles.

Locations.

The regional programs all offer the same core education while showcasing the local identity of each vibrant AI community. Whether you join us in-person from Edmonton, Montreal, Toronto, or virtually across Canada, the skills and network you gain at the Lab will kickstart your journey into AI.

Mila

The Montreal cohort is hosted at Mila.

Located in the heart of Quebec’s Artificial Intelligence ecosystem, Mila is a community of more than 1,000 researchers specializing in machine learning and dedicated to scientific excellence and innovation. Our mission is to be a global pole for scientific advances that inspires innovation and the development of AI for the benefit of all.

More about Mila

amii

The Edmonton cohort is hosted in partnership with Amii.

The Alberta Machine Intelligence Institute (Amii) is one of Canada’s three centers of AI excellence, advancing leading-edge research and growing AI capabilities in industry through advice and guidance, corporate training, talent development and advanced technology adoption.

More about Amii

The Toronto cohort is hosted in partnership with Vector Institute, CIFAR, and the Design Fabrication Zone at TMU.

CIFAR

CIFAR is a Canadian-based global research organization that convenes extraordinary minds to address the most important questions facing science and humanity. Since 2017, the CIFAR Pan-Canadian AI Strategy has been transforming AI research, training and innovation in Canada by building strong AI ecosystems and attracting, creating, and supporting talent across the nation.

More about CIFAR
Vector Institute

The Vector Institute is an independent, not-for-profit corporation dedicated to research in the field of artificial intelligence (AI), excelling in machine and deep learning. Established in March 2017 with generous support from the Government of Canada, Government of Ontario, and private industry, Vector works with institutions, industry, start-ups, incubators and accelerators to advance AI research and drive its application, adoption and commercialization.

More about the Vector Institute
Design Fabrication Zone

The Design Fabrication Zone (DFZ) is a community of interdisciplinary designers and fabricators. The DFZ supports the advancement of early-stage inventions, businesses and creative installations by providing business advice, technical expertise and hands-on prototyping. We provide *membership-based* support and services, in addition to hosting special opportunities for students and members of the public to learn and work in our downtown headquarters in The Creative School Innovation Studio at Toronto Metropolitan University.

More about the Design Fabrication Zone at TMU

Learn from experts in the field.

Rasha Kashef

Rasha Kashef

Assistant professor, Mila, University of Montréal

AI4Good Lab

Sanaz Mohammadjafari

PhD Candidate, Toronto Metropolitan University

Patrick Pilarski

Patrick Pilarski

Associate Professor, Canada CIFAR Artificial Intelligence Chair

Doina Precup

Dr. Doina Precup

Lab Founder, DeepMind, McGill University, Mila

Guillaume Rabusseau

Guillaume Rabusseau

Canada CIFAR AI Chair
Assistant Professor
Université de Montréal
Mila

Samrudhdhi Rangrej

Samrudhdhi Rangrej

PhD Candidate
McGill University

Siva Reddy

Siva Reddy

Facebook CIFAR AI Chair
Assistant Professor
McGill University
Mila

Matthew Schlegel

Matthew Schlegel

PhD Candidate
Amii, RLAAI
University of Alberta

Martha White

Martha White

Associate Professor
University of Alberta
RLAI and PI at AMII

FAQ.

Lab overview

The AI4Good Lab is an introductory program to machine learning for women and gender diverse people with some programming background and intermediate to no experience in machine learning. The Lab runs for 7 weeks during the summer and is split into 2 sections (the Bootcamp and Project weeks).

ML Bootcamp: The curriculum section is an intensive introductory bootcamp to machine learning and AI, which takes place over the first 4 weeks of the program. During this time, trainees attend lectures, talks, workshops, and networking events. Trainees will work in small groups with their assigned Teaching Assistant (TA) to practice what they’ve learned through exercises. The TAs are typically PhD students in Computer Science with specializations in Machine Learning or AI.
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Team projects: The Project section takes place over the last 3 weeks, during which trainees use the knowledge gained during the bootcamp and work in teams to develop an AI/ML prototype that addresses a social good issue. Teams are supported by their TAs and up to 3 mentors who work in AI.

The AI4Good Lab is part of Mila, the AI Institute in Montreal, and is a national initiative under the Pan-Canadian AI Strategy. Our founding partners are CIFAR and the OSMO Foundation, and we are supported by corporate, academic, and government organizations. The Montreal program is hosted by Mila, the Edmonton program is hosted in partnership with the Alberta Machine Intelligence Institute (Amii), and the Toronto program is hosted in partnership with CIFAR and Vector Institute with in-kind contributions from the Design Fabrication Zone at Toronto Metropolitan University.

We will accept 25 trainees per program for a total of 100 trainees. The small cohort size is chosen to ensure we can prioritize the growth and understanding of our trainees, while creating an environment that enables them to form meaningful relationships with their peers and educators.

The AI4Good Lab Program is entirely conducted in English. The application form is available in English and French.

Opportunities for individual learning are abundant, but the Lab addresses one of the biggest barriers for underrepresented individuals in the industry: access to mentorship and deep professional networks. We connect trainees with mentors and educators who are experienced AI practitioners in the industry. These experts have joined us to guide our trainees in their learning and career exploration, and are eager to welcome them into the AI community. 

Our goal is to open doors for those whose genders are underrepresented in the AI industry. We aim to achieve this in the following ways:

  • We accept trainees from a broad range of educational backgrounds and experiences.
  • Our program is tuition-free and we provide stipends and reimbursements to ensure equal access to the program.
  • Our education model centres curiosity-driven exploration and collaborative learning instead of competition and grades.

From mentorship and financial support to our educational ethos, this program is unmatched in its ambition to drive the culture of technology towards inclusion and representation.

Trainees come out of the program with:

  • A machine learning project to add to your technical portfolio or resumé, which is extremely helpful when applying to jobs or graduate programs
  • A technical introduction to all the main machine learning methods and a high level intro to AI applications (ex. ethics, healthcare, etc.)
  • Contacts within the AI industry, from researchers and PhD students to professionals working at our partners including Google, DeepMind, Vector Institute, Mila, Amii, etc. After the program alumni have direct access to job postings, events, etc. from our partners who are very interested in hiring and continuing to work with our alumni. The peer network also can't be understated; trainees have gone on to recommend each other for jobs, work on further projects together, or even develop close friendships.
  • Further access to the ecosystem of ML training offered through Mila, Vector Institute, and the Alberta Machine Intelligence Institute.

Program details

Both! The 2024 Lab will consist of 3 in-person cohorts and one fully virtual cohort. There will be online components for all cohorts and in-person components will be hosted in the three regional locations: Montreal, Toronto, Edmonton. Although it is strongly encouraged to take advantage of in-person networking opportunities, trainees will not be obligated to attend in-person events. Each location will determine which components of the program will be hosted in person.

All selected trainees will be given further details to allow for advanced planning. Regardless of the format, the Lab will continue to ensure the program’s accessibility for all trainees.

The 2024 program is running in Montreal, Edmonton, Toronto, and online from May 6th to June 26th. It is a full-time commitment that runs roughly during work hours, with scheduled breaks throughout the day. The hours vary across Canadian time zones so virtual activities can be run synchronously. Each day features four 90 minute sessions with 15 minute breaks in between and a one hour lunch. Due to the intensive nature of the projects, many trainees choose to work on their projects outside of Lab hours, including evenings and/or weekends. The program is designed to be fully immersive, so attendance at all lectures, talks, and workshops is required for all trainees.

For the Montreal, Edmonton, and Toronto programs, in-person attendance is strongly encouraged but not mandatory. The virtual program is accessible to anyone who is available during the program hours, a full-day schedule that runs roughly during work hours, and has a valid status to study or work in Canada. The Lab is exploring options for providing travel reimbursements to eligible trainees for certain events, however the Lab will not be able to fully fund trainees to move to a new city for the duration of the program. The stipend provided to all trainees at the end of the program may be used to cover living expenses and travel costs as well.

Like different classes for the same course, each regional program of the AI4Good Lab offers trainees the same value of education and opportunities for career advancement.

In 2024, the regional programs, including the online cohort, will overlap virtually for the foundational components of the program which includes lectures, speakers, and some networking events.

The distinct identities of each program arise from the different people involved; each program is run by a different team, has its own distinct cohort of trainees and TAs, and has some differences in the regionally hosted events. The Montreal program is run by the AI4Good Lab headquarters team at Mila, the Edmonton program is run in partnership with the Alberta Machine Intelligence Institute (Amii), and the Toronto program is run in partnership with Vector Institute and CIFAR. The Design Fabrication Zone at TMU assists in hosting the Toronto program though in-kind contributions (in-person work space and collaboration support). The online cohort will be supported by representatives from all three teams.

Each regional program will run its own study and project groups, as well as some events and social activities. Each location will determine which components of the program will be hosted in person and trainees will be notified of these decisions to allow for advanced planning.

The AI4Good Lab is a full-time commitment that includes daily attendance at mandatory events and intensive studying and/or group work. The hours vary across Canadian time zones so virtual activities can be run at the same time. Each day features four 90 minute sessions with 15 minute breaks in between, including a one hour lunch. In our experience, it is impossible to balance external responsibilities with full participation in the AI4Good Lab. If you are accepted and currently employed, we recommend receiving a leave of absence from your employer before accepting your spot.

Trainees will be expected to have access to a laptop/computer, high-speed internet (at least 70mbps download speeds), and earphones/headphones with a microphone. We may offer reimbursements for internet and other tech accessories to ensure all trainees can access the program without barriers.

At the AI4Good Lab, we prioritize a peer-driven, curiosity-based learning environment. No grades are given. Exercises will not be graded, but may be reviewed with TAs in work groups to enhance trainees understanding of the material. Attendance at lectures, talks, and workshops, however, is required for completion of the program.

All AI4Good Lab trainees who successfully complete the program will receive a certificate of completion. The AI4Good Lab is not an accredited institution and does not give official degrees.

Eligibility

The AI4Good Lab is for women and gender diverse people. The ideal applicant has a strong math background as well as some knowledge of Python (through coursework, personal projects, work experience, etc.).

All trainees must be current students at a post-secondary institution (University, college, etc.), or recent graduates. The program is an introductory program to machine learning and therefore not open to high school students, PhD students, or those who are already experienced in ML.

To be accepted, applicants must have a valid status in Canada (citizen, resident, study or work permit, etc.).

Our program is open to women and gender diverse people which includes women, all non-binary people, transgender men, and anyone with lived-experience of misogyny. We aim to reach those who would benefit from our mission in creating opportunities for people whose genders have historically been underrepresented in the AI industry. We are committed to listening and growing as the conversation about the nuances and complexity of gender and its intersections evolves. Please read Our Story to learn more about why the program exists to support women and gender diverse people.

The purpose of the program is to introduce machine learning to those who otherwise may not have the opportunity to explore it in-depth. We welcome applicants with diverse educational backgrounds! Trainees do not need to have a mathematics or computer science degree to succeed in the program, however it is an intensive and fast-paced program that requires previous knowledge of coding and math or the courage to pick up challenging concepts at a fast pace.

To keep up with the math concepts introduced in the lectures, trainees should at least be familiar with linear algebra, probability, and/or calculus. The lecturers will offer a basic overview of what you’ll need to know as it applies to machine learning with the assumption that you have some background knowledge to build off of. You may review the following topics for further insight: probability distribution, Bayesian estimation, basics of linear algebra, calculus derivatives, and regression.

The program does not teach its trainees to program, and all coursework is performed in Python. Trainees are expected to have prior experience coding in Python from activities such as coursework, personal projects, work experience, etc. If you are accepted into the program and feel uncertain about your Python skills, we recommend reviewing the basics before the start of the program.

Applicants do not need to know any machine learning prior to the program.

Unfortunately we are not available to review your background or application. If you are uncertain, we encourage you to apply.

To reduce economic barriers, all trainees are provided with a stipend. It is the responsibility of the trainee to ensure that their status in Canada enables them to receive this payment, which may be issued in the form of a Canadian cheque, and be able to report this income on their applicable taxes.

Economic support during the program is a key pillar that equalizes all trainees. It is a required part of the program.

Applications

When you apply, you have the option to indicate your preferred cohort — Edmonton, Montreal, Toronto or Online. The selection committee will categorize applicants into the appropriate cohort (Montreal, Edmonton, Toronto or Online) based on your location in Canada during the program. Although your preference may be taken into consideration, your cohort will be chosen to maximize the possibility of travel to in-person events while enabling you to collaborate virtually with peers in similar time-zones.

Applicants will need to log into their Google accounts to submit to our Google Form application.

To apply, applicants will need to submit their CV/Resume and a copy of their most recent transcript (unofficial transcripts are accepted). The application also consists of 3 short-essay questions (500 words max):

  1. Provide a brief description of why you'd like to join the Lab.
     
  2. The AI4Good Lab is a collaborative learning environment, where all trainees will work in groups throughout the curriculum and project phases. Please describe your experiences with collaborative learning and describe how you believe you will contribute to a supportive peer-learning environment.
     
  3. The AI4Good Lab is devoted to the use of AI as a tool for social good. Please describe how this mission speaks to you.

We encourage applicants to provide concrete examples for every essay question, drawing from personal history, projects, or activities that they were directly involved in to support their responses. We are unable to give feedback on individual applications.

Jan 10 - Jan 31 — Call for Applications

  • The call for applications opens in 2024 on January 10th, and closes on January 31st. You submit your application online.

Feb 1 - Mar 1 — Selection

  • After the call closes, the selection committee will process the applications. The selection committee consists of our founders, management team, and committee members from our partners.

March — Acceptance

  • All applicants will be notified of their selection status by mid-March at the latest. Those on the waitlist will be notified of selection on a rolling basis. Applicants who do not hear from us by March 15th may contact us at info@ai4goodlab.com to inquire as to the status of their application. The most common reason for a lack of response is because we received an incorrect email address from an applicant or our email was filtered as spam.
  • Each year we evaluate a competitive pool of applicants for limited spots, so we encourage rejected candidates to re-apply the following year. We are unable to give feedback or suggestions to those who are not accepted.

April — Preparation

  • The cohort is finalized and receives onboarding information to prepare for the program.

May 6 — Program Launch

  • Meet the AI4Good Lab community!

The waitlist does not have a specific order. Trainees are selected off the waitlist due to a variety of factors, and we cannot give further information on one’s chances of receiving a spot if they are placed on the waitlist.

Accepted applicants will have 4 business days to confirm their spot before the opportunity is passed to someone on the waitlist. Given the full-time requirements of the program, many trainees will need to make arrangements well in advance to attend. This means applicants who receive acceptances need to notify us ASAP if they are unable to attend the program in order to allow others the opportunity to plan ahead.

Costs

The only cost to attend the Lab is a $60 registration fee for accepted applicants to confirm their spot in the program.

There is no cost to apply and there are no tuition fees.

Every trainee receives a stipend after completion of the program. The minimum stipend amount is $1000, and the final amount is determined on a yearly basis by the end of the program. The final amount is determined by budgeting activities and is not dependent on the performance of the trainees.

Other questions? Please email us at info@ai4goodlab.com

Award winning projects.

See the project reports from last year that won the Accelerator Award. Get inspired to build your own project!

What our alumni say.

Kamun Karl Itaj

“I think the Lab gave me the confidence that I could actually work in the AI industry, even though I did not study computer science. I am grateful that the AI4Good Lab helped me with creating a vision for my future.”

Kamun Karl Itaj
AI4Good Lab Alumni

Joud El Shawa

“I am thankful that my first AI experience was developing an application for social good. This will remain with me throughout my future endeavours, where I will consider the ethical implications and social good aspects of any AI applications I work on.”

Joud El-Shawa
AI4Good Lab Alumni

Aurelie Nguyen

“The Lab is one of the best opportunities I've had in my life. I learned so much not only about AI, machine learning and the industry but also about myself. The connections made and the bonds formed with the other participants will be preciously kept for a long time.” 

Aurélie Nguyen
AI4Good Lab Alumni

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