Teachable Machine

Overview: Working with simple hand-drawn shapes on sticky notes, participants will teach a computer to tell two shapes apart by capturing training photos, training a model, and testing it live with their camera. Along the way, they'll discover how the quantity and diversity of training data directly affect a model's accuracy, gaining an intuitive, firsthand understanding of how machine learning actually works and what it takes to make AI smarter.

Instructions:

1. Go to teachablemachine.withgoogle.com/train

2. Select ‘Image Project’

3. Select ‘Standard Model’

4. Label Class 1 to <SHAPE 1> (this should reflect the name of the actual shape)

5. Take 20 photos of <SHAPE 1>

6. Label Class 2 to <SHAPE 2> (this should reflect the name of the actual shape)

7. Take 20 photos of <SHAPE 2>

8. Select ‘Train the model’

9. Test the model

- Present your sticky note to the camera with either Shape 1 or Shape 2 and see how confident it categorizes your shape!

Follow up questions for students:

How accurate is it? What can improve the model?

Follow Up Exercise (Optimizing the Model):

1. Take a total of 60+ photos for <SHAPE 1>

- Ensure photos are diverse and have different depths and angles of the shape.

2. Repeat for <SHAPE 2>

3. Select ‘Train the Model’

4. Test the model

Follow up questions for students:

Did the model performance improve? How does diversity of data change the outcome? How does the amount of data change the outcome?