Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 72 · 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.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) checking labels twice (d) clear, sharp examples
2. 2.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
3. 3.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
5. 5.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
6. 6.Which of these is good for training a model? (a) photos in many lights (b) copies of one photo (c) very few examples (d) testing on training data
7. 7.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) clear, sharp examples (d) very few examples
8. 8.🏷️ 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
9. 9.True or false: “photos in many lights” is good for training a model. (a) False (b) True
10. 10.True or false: “new data for testing” is good for training a model. (a) True (b) False
11. 11.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
12. 12.True or false: “labels added at random” is bad for training a model. (a) False (b) True
13. 13.🐶 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
14. 14.Which of these is bad for training a model? (a) examples of every type (b) copies of one photo (c) checking labels twice (d) many varied examples
15. 15.🏷️ 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
16. 16.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) slower to switch on (d) forget everything
17. 17.Which of these is good for training a model? (a) very few examples (b) new data for testing (c) copies of one photo (d) blurry, unclear photos
18. 18.Which of these is bad for training a model? (a) correct labels (b) testing on training data (c) clear, sharp examples (d) checking labels twice
19. 19.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
20. 20.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
21. 21.True or false: “wrong labels” is good for training a model. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) correct labels (b) checking labels twice (c) labels added at random (d) new data for testing
23. 23.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) copies of one photo (d) photos in many lights
24. 24.True or false: “testing on training data” is bad for training a model. (a) False (b) True
25. 25.Which of these is bad for training a model? (a) many varied examples (b) wrong labels (c) examples of every type (d) photos in many lights
26. 26.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it may get it wrong (d) it will turn yellow
27. 27.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
28. 28.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) testing on training data (b) only one kind of example (c) blurry, unclear photos (d) checking labels twice
30. 30.True or false: “wrong labels” is bad for training a model. (a) False (b) True
31. 31.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
32. 32.True or false: “examples of every type” is bad for training a model. (a) False (b) True
33. 33.🏷️ 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
34. 34.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
35. 35.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
36. 36.Which of these is good for training a model? (a) checking labels twice (b) copies of one photo (c) labels added at random (d) very few examples
37. 37.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
38. 38.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
39. 39.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
40. 40.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True

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