Machine Learning for Kids Worksheet

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

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