“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

Course schedule

  • This course runs from March 20 - April 26, 2023 (6 weeks)
  • Lectures are held virtually on Mondays from 10:00 AM - 12:00 PM ET
  • Tutorials are held virtually on Wednesdays from 10:00 AM - 12:00 PM 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 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 in part by a generous contribution from CIFAR.

Please note that this course is intended for stay-at-home parents and caregivers, or parents on maternity leave and/or paternal leave to expand and accelerate access to knowledge in AI. The self-identification data collected will be used for the purpose of confirming eligibility for the program, which is designed to meet the criteria of Special Programs under the Ontario Human Rights Code to assist eligible individuals in achieving equal opportunity in the field of AI.  

If you have any questions, please email us at learn@vectorinstitute.ai with the subject line “Caregivers and Machine Learning”.