Caregivers and Machine Learning
- The course runs from March 20, 2023 – April 26, 2023 (6 weeks).
- Lectures are held on Mondays from 10am-12pm ET.
- Tutorials are held on Wednesdays from 10am – 12pm ET.
- The course is designed to be completed in 8-10 hours per week.
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.
- 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 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.
Mothers and primary caregivers interested in acquiring skills in Machine Learning. Priority will be given to Canadian residents and citizens.
Level of Training
- 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 firstname.lastname@example.org