Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 210 · 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.🐶 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
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
3. 3.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
4. 4.True or false: “testing on training data” is good for training a model. (a) True (b) False
5. 5.Which of these is good for training a model? (a) blurry, unclear photos (b) many varied examples (c) testing on training data (d) very few examples
6. 6.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) testing on training data (d) checking labels twice
7. 7.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
9. 9.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) examples of every type (d) labels added at random
10. 10.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
11. 11.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) checking labels twice (d) many varied examples
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
13. 13.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
14. 14.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it may get it wrong (c) it will be perfect (d) it will shut down
15. 15.🔍 Which is the odd one out? (a) copies of one photo (b) wrong labels (c) very few examples (d) clear, sharp examples
16. 16.Which of these is good for training a model? (a) copies of one photo (b) checking labels twice (c) blurry, unclear photos (d) testing on training data
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
18. 18.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
19. 19.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) clear, sharp examples
20. 20.True or false: “copies of one photo” is good for training a model. (a) True (b) False
21. 21.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
22. 22.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
23. 23.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
24. 24.True or false: “checking labels twice” is good for training a model. (a) True (b) False
25. 25.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
26. 26.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
27. 27.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
28. 28.Which of these is good for training a model? (a) blurry, unclear photos (b) labels added at random (c) only one kind of example (d) correct labels
29. 29.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
30. 30.🔍 Which is the odd one out? (a) checking labels twice (b) wrong labels (c) new data for testing (d) photos in many lights
31. 31.🏷️ 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
32. 32.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
33. 33.🔍 Which is the odd one out? (a) checking labels twice (b) clear, sharp examples (c) many varied examples (d) testing on training data
34. 34.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
35. 35.True or false: “photos in many lights” is good for training a model. (a) True (b) False
36. 36.True or false: “correct labels” is good for training a model. (a) False (b) True
37. 37.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
38. 38.🔍 Which is the odd one out? (a) labels added at random (b) photos in many lights (c) clear, sharp examples (d) correct labels
39. 39.🏷️ 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
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam

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