Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 7 · 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.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
2. 2.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it will be perfect (d) it may get it wrong
3. 3.Which of these is good for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) wrong labels (d) testing on training data
4. 4.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
5. 5.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
6. 6.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) examples of every type (d) copies of one photo
7. 7.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
8. 8.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) labels added at random (d) examples of every type
9. 9.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) clear, sharp examples (d) labels added at random
10. 10.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
11. 11.True or false: “examples of every type” is bad for training a model. (a) False (b) True
12. 12.Which of these is good for training a model? (a) correct labels (b) testing on training data (c) blurry, unclear photos (d) wrong labels
13. 13.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
14. 14.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) photos in many lights (d) wrong labels
15. 15.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
16. 16.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
17. 17.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
18. 18.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) new data for testing (d) labels added at random
19. 19.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
20. 20.Which of these is good for training a model? (a) checking labels twice (b) testing on training data (c) wrong labels (d) labels added at random
21. 21.🏷️ 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
22. 22.True or false: “only one kind of example” is good for training a model. (a) False (b) True
23. 23.True or false: “wrong labels” is good for training a model. (a) False (b) True
24. 24.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) testing on training data (d) labels added at random
25. 25.🔍 Which is the odd one out? (a) new data for testing (b) wrong labels (c) labels added at random (d) testing on training data
26. 26.Which of these is good for training a model? (a) blurry, unclear photos (b) only one kind of example (c) very few examples (d) photos in many lights
27. 27.🔍 Which is the odd one out? (a) blurry, unclear photos (b) wrong labels (c) very few examples (d) clear, sharp examples
28. 28.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
29. 29.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) checking labels twice (d) very few examples
30. 30.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
32. 32.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
33. 33.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
34. 34.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) a song about dogs (d) one single photo
35. 35.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
36. 36.True or false: “very few examples” is good for training a model. (a) True (b) False
37. 37.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
38. 38.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
39. 39.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) examples of every type (d) labels added at random
40. 40.True or false: “correct labels” is bad for training a model. (a) False (b) True

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