Caregivers and Machine Learning

This free 6-week online Machine Learning (ML) course is designed to tap into the talent of stay-at-home parents on maternity and/or parental leave to facilitate their participation in the rapidly growing field of artificial intelligence (AI). It is an introduction to core concepts in ML with applications in computer vision and natural language processing. This course is developed and delivered by faculty and staff at the Vector Institute and includes funds generously provided by Vector and CIFAR to help cover the cost of the program and child care.
“This was… a life-changing course for me (and likely many other mothers). This was the exact confidence boost and filling of a knowledge gap that I needed to be able to explore these concepts on my own after the class.” – Baraa Al-Khazarji, Assistant Professor in McMaster’s Department of Kinesiology
Recognizing the underrepresentation of women (cisgender and transgender), non-binary and Two-Spirit people in the field of AI, preference will be given to applicants who identify as such.

Course Schedule

  • The course runs from March 20, 2023 – April 26, 2023 (6 weeks).
  • Lectures are held virtually on Mondays from 10am-12pm ET.
  • Tutorials are held virtually on Wednesdays from 10am – 12pm ET.
  • The course is designed to be completed in 8-10 hours per week.

Learning Outcomes

By the end of this course, students will be able to explain key ML concepts and identify use cases and applications. Students will develop a baseline understanding of machine learning methods and how they can produce value for an organization, enabling them to make recommendations for new technologies and solutions. This course will also emphasize ethics and the responsible use of AI. Students will also hear from women leaders in AI on their career paths and gain access to networking opportunities.

Proposed Content

  • Supervised learning: k-nearest neighbors and decision trees, linear regression, logistic and softmax regression, neural networks, convolutional neural networks for image classification, text classification, and NLP applications.
  • Unsupervised learning: probabilistic models, principal component analysis and relation to K-means, matrix factorization, and expectation maximization.
  • Introduction to reinforcement learning.
  • Fairness in AI.
  • Keynote presentations by women leaders in AI and related networking opportunities.


Juan Felipe Carrasquilla Álvarez
Faculty Member, Vector Institute
Adjunct Faculty, University of Waterloo
Canada CIFAR AI chair


This course will be delivered virtually in two sessions per week:

1. A lecture format to cover basic theory, and

2. A tutorial format that includes practical coding exercises.

All sessions will be delivered live, and recordings of those sessions will be made available.


In order to receive a certificate of completion, students must:

  • Attend 80% of the lectures, and
  • Turn in assignments including theoretical exercises, practical coding, and/or reports.

Intended Audience

Mothers and primary caregivers on parental-leave interested in acquiring skills in Machine Learning. Priority will be given to Canadian residents and citizens.

Level of Training


Suggested Prerequisites

  • Basic understanding of linear algebra, calculus, and probability. (E.g. 1st year University mathematics, including matrix multiplication, vectors, first order derivatives, linear regression, probability distributions)
  • Basic coding skills. (E.g. experience programming in any language, including creating variables, importing libraries, basic arithmetic operations, loops, conditional statements, functions. A review of Python basics can be completed here).

Proposed Financial Arrangements

This course is fully funded by Vector Institute’s supporters, and includes a child care bursary of $500 funded by a generous contribution from Vector and CIFAR.

Questions about the course? Please email

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