WORKSHOP

Training the Machine

Discover how machines recognize faces, emotions, and language, and why it is important that all communities are represented in the digital future.

Feb 28, 2026
AGES: 8 – 17
COST: FREE
FORMAT: IN-PERSON

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About this workshop

In celebration of Black History Month, this workshop invites students to explore how diversity, culture, and representation influence the technologies shaping our world today. Students learn that Artificial Intelligence is not neutral—it reflects the data it is trained on—and that inclusive data leads to smarter, fairer, and more accurate AI systems. Through hands-on learning and real-world examples, learners discover how machines recognize faces, emotions, and language, and why it is important that all communities are represented in the digital future.

In the Beginner Track, students train an emotion-recognition AI model using images and expressions, gaining firsthand experience in how data quality and diversity affect machine learning outcomes, while in the Advanced Track, students design the personality, purpose, and conversation flow of a chatbot using prompt engineering and blueprint planning, laying the foundation for building intelligent, responsible, and user-friendly AI systems in future workshops.

This workshop is FREE, and all learning materials are provided.

With funding from:

AI & Data Training

Theme: Learning Through Data

Students dive deeper into AI training by creating a more sophisticated emotion-detection model. They learn the critical concept that AI learns from data — the more examples you give it, the smarter it becomes. Students explore what makes good data vs. bad data and understand why diversity in training examples matters. The session focuses on building a 3–4 class emotion recognition model (Happy, Sad, Surprised, and optionally Angry).

Key Activities

  • Reviewing Month 1 progress
  • Learning about data quality (lighting, angles, backgrounds)
  • Collecting 20–30 photos per emotion class
  • Training emotion detection model with proper data
  • Testing model accuracy with different expressions
  • Exporting trained models for next month

Learning Outcomes

Students will:

  • Test and evaluate model accuracy
  • Understand how AI learns from data
  • Recognize the importance of diverse training data
  • Collect and organize quality training images
  • Train a 3–4 class emotion detection model

Register by location

London

Western University, Spencer Engineering Building
501 Southdale Road West

Training the Machine

Feb 28, 2026
11:00 am 3:00 pm EST
IN-PERSONFREE

Ottawa

University of Ottawa
160 Louis-Pasteur Private, Ottawa, ON K1N 6N5

Training the Machine

Feb 28, 2026
10:00 am 2:00 pm EST
IN-PERSONFREE

Durham

Ontario Tech University, Main Oshawa Campus
2000 Simcoe Street North, Oshawa, ON

Training the Machine

Feb 28, 2026
11:00 am 3:00 pm EST
IN-PERSONFREE

Waterloo

University of Waterloo
200 University Avenue West, Waterloo, ON

Training the Machine

Feb 28, 2026
11:00 am 2:00 pm EST
IN-PERSONFREE

Brampton

Sheridan College, Davis Campus
7899 McLaughlin Road, Brampton, ON

Training the Machine

Feb 28, 2026
11:00 am 3:00 pm EST
IN-PERSONFREE

Montreal

Concordia University
1455 Boulevard De Maisonneuve Ouest, Montreal, QC

Training the Machine

Feb 28, 2026
11:00 am 3:00 pm EST
IN-PERSONFREE

Vancouver

Centre for Digital Media
685 Great Northern Way, Vancouver, BC

Training the Machine

Feb 28, 2026
12:00 pm 2:00 pm PST
IN-PERSONFREE

Maritimes

UNB Faculty of Law
41 Dineen Drive, Fredericton, NB E3B 9V7

Training the Machine

Feb 28, 2026
11:00 am 2:00 pm AST
IN-PERSONFREE

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