Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 540 · 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.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it may get it wrong (d) it will be perfect
2. 2.True or false: “many varied examples” is bad for training a model. (a) False (b) True
3. 3.Which of these is bad for training a model? (a) checking labels twice (b) photos in many lights (c) examples of every type (d) blurry, unclear photos
4. 4.True or false: “new data for testing” is bad for training a model. (a) False (b) True
5. 5.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
6. 6.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) wrong labels (d) very few examples
7. 7.True or false: “testing on training data” is good for training a model. (a) False (b) True
8. 8.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
9. 9.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
10. 10.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
11. 11.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
12. 12.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) very few examples (d) checking labels twice
13. 13.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) only one kind of example (d) examples of every type
14. 14.Which of these is bad for training a model? (a) many varied examples (b) very few examples (c) clear, sharp examples (d) new data for testing
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
16. 16.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
17. 17.True or false: “wrong labels” is bad for training a model. (a) True (b) False
18. 18.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
19. 19.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
20. 20.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) correct labels (d) only one kind of example
21. 21.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) checking labels twice (d) correct labels
22. 22.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) correct labels (d) wrong labels
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
24. 24.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) correct labels (d) blurry, unclear photos
25. 25.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
26. 26.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) testing on training data (d) many varied examples
28. 28.True or false: “examples of every type” is bad for training a model. (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) correct labels (d) copies of one photo
30. 30.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) very few examples (d) only one kind of example
31. 31.True or false: “many varied examples” is good for training a model. (a) False (b) True
32. 32.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
33. 33.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
35. 35.🔍 Which is the odd one out? (a) new data for testing (b) wrong labels (c) examples of every type (d) photos in many lights
36. 36.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
37. 37.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
38. 38.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
39. 39.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
40. 40.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False

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