Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 297 · 40 questions

AI tip (Show, label, learn): Machine learning learns patterns from many labelled examples — a label is the right answer tag on each example. Good for training: many varied examples, correct labels, every type the model will meet, and separate new data for testing. Bad for training: very few examples, wrong or random labels, only one kind of example, blurry examples, testing on the training data.

1. 1.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
2. 2.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the price (d) the right answer tag
3. 3.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
4. 4.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
6. 6.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
7. 7.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) very few examples (d) many varied examples
8. 8.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
9. 9.🔍 Which is the odd one out? (a) new data for testing (b) blurry, unclear photos (c) wrong labels (d) only one kind of example
10. 10.True or false: “wrong labels” is bad for training a model. (a) False (b) True
11. 11.Which of these is good for training a model? (a) clear, sharp examples (b) very few examples (c) blurry, unclear photos (d) copies of one photo
12. 12.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it may get it wrong (d) it will turn yellow
13. 13.🔍 Which is the odd one out? (a) only one kind of example (b) many varied examples (c) new data for testing (d) clear, sharp examples
14. 14.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) blurry, unclear photos (d) correct labels
15. 15.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) wrong labels (d) many varied examples
16. 16.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) only one kind of example (d) checking labels twice
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
18. 18.True or false: “very few examples” is good for training a model. (a) True (b) False
19. 19.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
20. 20.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
21. 21.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
22. 22.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
23. 23.True or false: “examples of every type” is bad for training a model. (a) True (b) False
24. 24.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) many varied examples (d) labels added at random
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
26. 26.🔍 Which is the odd one out? (a) examples of every type (b) correct labels (c) wrong labels (d) photos in many lights
27. 27.True or false: “many varied examples” is bad for training a model. (a) False (b) True
28. 28.Which of these is bad for training a model? (a) examples of every type (b) new data for testing (c) photos in many lights (d) very few examples
29. 29.🔍 Which is the odd one out? (a) testing on training data (b) examples of every type (c) very few examples (d) wrong labels
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
31. 31.True or false: “copies of one photo” is good for training a model. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) correct labels (d) very few examples
33. 33.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) one single photo (d) many labelled photos
34. 34.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
35. 35.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
36. 36.True or false: “new data for testing” is good for training a model. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) labels added at random (b) correct labels (c) testing on training data (d) only one kind of example
38. 38.True or false: “wrong labels” is good for training a model. (a) True (b) False
39. 39.True or false: “many varied examples” is good for training a model. (a) False (b) True
40. 40.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training

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