ML Classes and Internships for Black & Indigenous Students and Recent Grads

Vector Institute is committed to developing programming for Black and Indigenous students, postdocs, and recent graduates to build research and expand career pathways in AI for historically underrepresented groups. In 2021, Vector launched a machine learning course and AI internships to accelerate and expand access to knowledge of AI to Black and Indigenous students and postdocs in Canada. The programming provides relevant, hands-on learning and work experience in machine learning and AI. It is designed to meet the criteria of Special Programs in line with the values of the Ontario Human Rights Code to assist underrepresented groups to achieve equal opportunity in the field of AI.

Vector Applied Internships for Black & Indigenous Students

Summer 2023 applied internship applications are now open! Applications close February 13th, 2023, at 12:00PM EST.

Work full or part-time alongside professional staff at Vector on AI and data centric projects. Internships may be remote or in-person.

Summer 2023 internships include:

  • Applied Machine Learning Intern: Develop reusable software to apply and scale research breakthroughs in machine learning.
  • Scientific Writer & Communications Intern: Dig up and spotlight the latest cutting-edge AI research in Canada to celebrate Canada’s world-class AI talent.
  • AI Project Management Intern: Support AI projects which enable industry sponsors and health partners to develop, deploy and scale AI applications.
  • FastLane Program Analyst Intern: Support the development and implementation of programming for medium-sized enterprises and support the growth of the FastLane program, accelerating the adoption of AI in small and medium-sized enterprises throughout Canada.
  • AI Education Intern: Build ML education programs, which enable widespread AI adoption for industry sponsors.
  • Equity, Diversity, & Inclusion Analyst Intern: Conduct research on EDI in AI and make recommendations for the future of Vector’s programming for Black & Indigenous students and internship program.
  • Market Research & Analyst Intern: Conduct market research and analysis on AI talent and education trends and make recommendations on the future of Vector’s workforce development programming.

Apply

Internships are 16 to 32 weeks in length and run during the Fall, Winter, and Summer terms.  

Term Fall Winter Summer
Application Period June October January 30 – February 13 @ 12:00PM EST, 2023
Internship Term September – December January – April May 15 – September 1, 2023

Summer 2023 applications are now open! Applications close February 13th, 2023, at 12:00PM EST.

At the Vector Institute we are committed to driving excellence and leadership in Canada’s knowledge, creation, and use of AI to foster economic growth and improve the lives of Canadians. We strive for greater inclusion in the programs and culture that we build by welcoming and encouraging applications from all qualified candidates.  In order to relieve disadvantage and alleviate the under-representation of Black and Indigenous people in AI, and to attempt to achieve equal opportunity in the field of AI, preference in hiring will be given to qualified Black and Indigenous persons, studying at or having recently graduated from a Canadian post-secondary institute, who self-identify as such in the application process. This initiative is designed to meet the criteria of a special program under the Ontario Human Rights Code.  

Apply

Vector & BPTN

Vector is collaborating with the Black Professionals in Tech Network (BPTN), who has partnered with top Canadian companies who will hire a total of 375 early career, Black professionals each year for the next 3 years. This includes internships and co-ops. Applied intern roles have also been posted on the BPTN career portal. Feel free to set up a profile on the BPTN network today for internship opportunities in tech roles across the country.

Award Opportunity for Black and Indigenous Interns

Upon completion of the applied or research internship, Black and Indigenous interns who are beginning or continuing their studies at a post-secondary institution in Canada will be eligible to apply for a $1,500 award to help offset the cost of their education.

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Interested in interning at Vector? Join our mailing list.

Black & Indigenous Research Internship Program

Applications for the Black & Indigenous Research Internship Program are now closed. Sign up to be notified when applications open in the future.

This program designed to meet the criteria of Special Programs in line with the values of the Ontario Human Rights Code to assist underrepresented groups to achieve equal opportunity in the field of AI.

Summer 2023 Projects for Black & Indigenous Post-Secondary Students in Canada

Information Geometric Approaches in Reinforcement Learning

What do techniques from information geometry and dynamical systems teach us about the behaviour of reinforcement learning algorithms?

Faculty: Amir-Massoud Farahmand & Juan Felipe Carrasquilla Alvarez

Level of Intern: Undergrad, Master’s, or PhD

Project Summary: We will study geometric approaches to the optimization of reinforcement learning (RL) algorithms using numerical and analytical tools. The main goal is to make progress towards our understanding of the training dynamics of RL algorithms as well as explore the possibility to establish rigorous convergence results for RL algorithms in the overparameterized regime.

Machine Learning Medical Directive for the emergency department (MLMD)

What is the best predictor for the different medical tests that the patient needs at SickKids emergency department between triage and seeing a medical provider?

Faculty: Anna Goldenberg

Level of Intern: Master’s, or PhD

Project Summary: Increased patient volumes and extended periods of stay in emergency departments (EDs) are typical healthcare challenges associated with suboptimal patient care. The Machine Learning Medical Directives (MLMD) strategy aims to use AI/ML to help determine the need for downstream medical testing immediately following patient triage and automate test ordering thus reducing wait time for the patients in order to improve ED efficiency. The AI in Medicine (AIM) initiative team at SickKids co-chaired by Drs Anna Goldenberg and Mjaye Mazwi is looking for a junior machine learner to join the team and work on this project.

The intern’s duties include, but are not limited to:

  • Contribute to the enhancement of the current preprocessing pipeline.
  • Re-think the implementation of different data encoding strategies.
  • Contribute to the training and validation of various MLMD models.

Required Skills or Experience (academic or work experience):

  • Natural language processing techniques.
  • Processing data and training different ML models.
  • Developing and implementing algorithms for processing large datasets.
  • Proficiency with Python and shell scripting.
  • Working collaboratively with the different AIM members.

Preferred Skills or Experience (academic or work experience):

  • Developing ML models based on clinical or biomedical data.
  • Strong understanding of statistics.
Building Foundation Models for Medical Imaging

Can we develop large foundation models for zero-shot learning in medical domains using small datasets?

Faculty Member: Bo Wang

Level of intern: Undergrad, master’s, or PhD

Project Summary: Foundation models (e.g., BERT, GPT-3, ViT, and Stable Diffusion) have transformed current artificial intelligence (AI) research by providing big models that can be adapted to a wide range of downstream tasks. These models are trained on large-scale and diverse datasets. However, such foundation models have not emerged in the medical image domain because most current datasets are relatively small. We aim to construct large-scale medical image datasets from multiple institutions and develop general AI models that can serve as the backbone for various medical image analysis tasks, such as disease diagnosis, tumor burden quantification, and treatment monitoring.

Human-in-the-loop Machine Learning

We aim to: Supercharge prompt engineering with human assistance.

Faculty: Jimmy Ba

Level of Intern: Undergrad, Master’s, or PhD

Project Summary: Modern machine learning has primarily been driven by high-quality, well-curated datasets. Yet during deployment, the input data often differ significantly from the training time, leading to poor generalization performance. One way to overcome poor generalization is to collect human advice during the test time to guide the model toward the desired behavior. In this project, we aim to reproduce the infrastructure and the user interface used to collect human feedback from the recent publications.

Theory for ML Algorithms

Can we theoretically explain the presence of feature learning in non-convex models like deep neural networks?

Faculty: Murat A. Erdogdu

Level of Intern: Undergrad, Master’s, or PhD

Project Summary: Non-convex optimization and sampling are building blocks in modern machine learning due to the structural properties of popular statistical models. Owing to their key role and empirical success in numerous learning tasks, they have been a major focus of recent research. Many important characteristics of machine learning models such as generalization and fast-trainability are inherited from these non-convex methods; thus, a good understanding of these algorithms are crucial.

The main purpose of this project is to improve our theoretical understanding on non-convex algorithms, which are ubiquitous in machine learning. Students will pursue several principled directions. The overall project plan can be broken into three sections, to be pursued simultaneously: (1) Theoretical analysis of commonly used non-convex optimization algorithms; (2) Design of efficient optimization algorithms for machine learning; (3) Applying these methods to real problems.

Neural Network Training Dynamics

What do training dynamics tell us about the trustworthiness of a deployed machine learning model?

Faculty: Nicolas Papernot

Level of Intern: Undergrad, Master’s, or PhD

Project Summary: Current methods for trustworthy machine learning often impose constraints on either the model architecture or the loss function; this inhibits their usage in practice. In contrast to prior work, we show that, for instance, state-of-the-art selective classification performance can be attained solely from studying the (discretized) training dynamics of a model. We propose a general framework to leverage training dynamics for increasing trust in machine learning.

Making sense of probabilistic predictions

What do probabilistic predictions teach us about the trustworthiness of machine learning models and how do we evaluate them properly?

Faculty: Yaoliang Yu

Level of Intern: Undergrad, Master’s, or PhD

Project Summary: Most modern neural networks produce a probabilistic prediction, often through inserting a soft-max(ish) layer at the end to turn real-valued scores into probability estimates. What does it mean when an algorithm makes a probabilistic prediction, e.g. an object in a test image is a dog with 75% of chance, a cat with 10% of chance, or a tiger with 15% of chance? A natural requirement, known as calibration, is that conditioned on the predicted probability, the true probability should indeed coincide with the predicted one. Surprisingly, recent studies have found that raw probability estimates may no longer be well-calibrated. In this project we aim to closely examine the role calibration plays in modern neural network training and testing, as well as to understand and possibly re-calibrate such probabilistic estimates so that they indeed conform to our intuition and carry meaningful information that can be tested and compared.

Who should apply?

Vector’s Black & Indigenous Research Internship Program is offered to individuals who meet the following criteria:

  • You are a self-identified Black or Indigenous (Indigenous ancestry or heritage – First Nations (status or non-status), Métis or Inuit) post-secondary student or Postdoctoral Fellow at a Canadian university or college;
  • You are studying in a STEM program (Science, Technology/Computer Science, Engineering, Math);
  • You have, at a minimum, completed your second year in an undergraduate program at a Canadian university;
  • You have completed an Intro to Machine Learning Course.

Award Opportunity for Black and Indigenous Interns

Upon completion of the applied or research internship, Black and Indigenous interns who are beginning or continuing their studies at a post-secondary institution in Canada will be eligible to apply for a $1,500 award to help offset the cost of their education.

Did you know?

Vector also offers research internships open to students across the globe. Vector Faculty participating in the Black & Indigenous Research Internship Program may take on additional research interns who are not Black & Indigenous and/or not currently enrolled at a Canadian post-secondary institution. Learn more about Vector’s research internships here.

Interested in interning at Vector? Join our mailing list.

Introduction to Machine Learning for Black and Indigenous Students and Postdocs

50 Black & Indigenous post-secondary students from across Canada are now participating in Vector’s Intro to ML course and the application period for the Fall 2022 course has now closed. Sign up to be notified when the Intro to ML Course runs again.

Participate in a free nine week online Introduction to Machine Learning course starting October 2022. The course will introduce common machine learning algorithms such as linear and logistic regression, random forests, decision trees, neural networks, support vector machines, and boosting. It will also offer a broad overview of model-building and optimization techniques that are based on probabilistic building blocks which will serve as a foundation for more advanced machine learning courses. Featuring two hours of lectures/workshops per week, as well as weekly tutorials to help you further assimilate the material, this course will provide a rich, hands-on learning environment.

At the conclusion of the course, you will complete a capstone project and presentation. A Best Capstone Award of $500 will be given to the top three capstone papers and presentations. Check out last year’s capstone presentations on our YouTube channel.

As a part of this program, you will:

  • Receive a comprehensive introduction to Machine Learning;
  • Gain access to Vector’s weekly research talks with world class faculty and researchers;
  • Participate in career development programming with companies driving AI adoption;
  • Acquire in-demand skills and build meaningful connections to people working and doing research in AI;
  • Gain foundational knowledge and preparation in ML to apply to a Vector internship for Black and Indigenous students;
  • Receive a $500 award upon successful completion of the course; and
  • Earn a professional development certificate of completion.

This course is being offered to individuals who meet the following criteria:

  • You are a self-identified Black or Indigenous student or postdoc (Indigenous ancestry or heritage – First Nations (status or non-status), Métis or Inuit);
  • You have completed at least one year of post-secondary education (i.e., you are in second-year or higher in your undergraduate degree, or in your master’s, PhD, or postdoctoral studies)
  • You are studying at a Canadian university or college;
  • You are studying in a STEM program (Science, Technology/Computer Science, Engineering, Math) or adjacent disciplines such as Business, Economics or Life Sciences;
  • You have coding experience with Python or R*. You have taken statistics or math at the post-secondary level; and
  • You are committed to completing coursework on a weekly basis and participating in class.

*If you are interested in applying for the course but have no or limited coding experience in Python, please submit an application. Vector also runs a 3 day Excel to AI course that you may be able to take, subject to space in the course, instead of the Introduction to Machine Learning course or prior to the course. See the FAQ below for more details.

Please note that enrolment in the Introduction to Machine Learning course is capped at approximately 50 students; a minimum of 10 students is required to run the course. Given our limited enrolment capacity, not all applicants will be offered a spot in the course. We will connect with all applicants no later than September 16, 2022 to let you know if you have been offered a spot in the course.

When does the course run?

The course is tentatively scheduled to run beginning the week of October 2nd to the week of November 13th, with classes held Wednesdays from 3pm to 5pm EST and tutorials running Thursdays from 3pm to 5pm EST. Capstone project presentations will take place after the course has concluded on November 30th. All classes and tutorials will be held online. 

The application period for the Fall 2022 course has now closed.

Spotlight on the Inaugural Intro to ML Course for Black & Indigenous Students 

Student Testimonials:

“Regardless of your Machine Learning knowledge level, you will find something to learn or it will be a solid refresher.”- Patrick Adjei

“The Introduction to ML course offered by Vector Institute is a great, flexible and insightful program. It exposes one to the fundamentals (both theory and practical) in Machine Learning, ranging from supervised learning, unsupervised learning, etc. I highly recommend this program.” – Oluwabukola (Grace) Adegboro

“The passion and effort that the instructors at the Vector institute have, combined with their genuine interest in helping their students is quite unmatched, making for a unique learning experience.”- Mogtaba Alim

Read more about the student experience in this blog post and watch the capstone presentations that won the Best Capstone Presentation & Paper award.

If you would like to stay in touch with Vector and learn more about upcoming opportunities, please subscribe to our mailing list.

Course FAQS

Can I use this course for degree credit?

This course is a professional development offering and cannot be used for degree credit.  While Vector offers professional development to industry, health partners and students, it is not an accredited academic institution. You will receive a certificate of completion at the end of the course that you can include on your resume and LinkedIn profile.

Will there be assignments that are evaluated?

There will be four course assignments and a capstone project that will be reviewed and graded by the instructor and TA to provide you with feedback on your progress. Capstone projects are also a great way to showcase what you have learned in the course and demonstrate your knowledge to prospective employers.

An award for the Best Capstone Paper & Presentation, valued at $500 each for a total of $1,500, will be awarded to the top three capstones.

Why is Vector offering a free course on Machine Learning for Black and Indigenous students?

There are challenges facing Black and Indigenous people entering the field of Artificial Intelligence, STEM, and tech in general. Both statistical data on workforce participation (1)  and anecdotal evidence reflects that there are real barriers that require action. The purpose of Vector’s Introduction to ML course and internship programming is to increase the opportunities to build research and career pathways in AI for Black and Indigenous students in Canada. This initiative is designed to meet the criteria of Special Programs in line with the values of the Ontario Human Rights Code (the Code), to assist underrepresented groups to achieve equal opportunity in the field of AI.

 

(1) Based on 2016 Census data, Indigenous people make up 4% of adults in Canada but less than 2% of people working in STEM occupations are Indigenous (Analysis of 2016 Census data. Statistics Canada, “Data Products, 2016 Census.”). According to a Brookfield Study (2019) entitled Who are Canada’s Tech Workers? Indigenous people’s participation in tech occupations is less than half of non-Indigenous people’s participation in the field (2.2% vs. 5.2%). Similarly, Black people reflect 2.6% of the tech workforce but 3.5% of the population. A MaRS Discovery District analysis using a survey dataset powered by Fortay and Feminuity of tech workers in Toronto found that Black tech workers were more likely to report lower levels of diversity, inclusion and belonging compared to White, Asian, and other Visible Minorities.
I have more questions about the course. Who do I email?

Please don’t hesitate to contact internships@vectorinstitute.ai if you have further questions about the application process or the course.

I am a student at a university/college outside Canada. Am I eligible?

No, this course is open to students studying at a Canadian university or college.

I’m not in a STEM or STEM adjacent program OR I have limited to no coding experience. Can I still apply for this course?

We encourage you to submit an application. When applying, Vector will assess your technical proficiency based on your reported experience levels in Python or R, Excel, Statistics, Linear Algebra, and Multivariate Calculus. If you have limited proficiency in Python, Vector may offer you a spot in our Excel to AI course.

Excel to AI is a 3-Day course, which runs 2 hours each day, and introduce Python as a tool for exploring basic Machine Learning capabilities. This course will expand the possibilities to include tasks with unstructured data, larger datasets, and more efficient computing. 

If space is available in an upcoming Excel to AI course, you may be offered a spot in the course.

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