Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 131 · 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? “Last chance! Free gift card inside” (a) spam (b) not spam
2. 2.🏷️ 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
3. 3.🏷️ 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
4. 4.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
5. 5.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) wrong labels (d) only one kind of example
6. 6.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
7. 7.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
8. 8.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
9. 9.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
10. 10.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
11. 11.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will shut down (c) it will turn yellow (d) it will be perfect
12. 12.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) wrong labels (d) copies of one photo
13. 13.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
14. 14.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) many varied examples (d) wrong labels
15. 15.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
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) not spam (b) spam
17. 17.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
18. 18.True or false: “testing on training data” is good for training a model. (a) True (b) False
19. 19.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
20. 20.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
21. 21.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) photos in many lights (d) wrong labels
22. 22.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) copies of one photo (d) clear, sharp examples
23. 23.Which of these is good for training a model? (a) testing on training data (b) very few examples (c) copies of one photo (d) examples of every type
24. 24.True or false: “labels added at random” is good for training a model. (a) True (b) False
25. 25.True or false: “copies of one photo” is good for training a model. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) correct labels (d) only one kind of example
27. 27.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
28. 28.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
29. 29.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) clear, sharp examples (d) checking labels twice
30. 30.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
31. 31.🏷️ 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
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
33. 33.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) many varied examples (d) copies of one photo
34. 34.🏷️ 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
35. 35.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
36. 36.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) many labelled photos (d) a list of rules only
37. 37.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) examples of every type (d) photos in many lights
38. 38.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
39. 39.🔍 Which is the odd one out? (a) copies of one photo (b) wrong labels (c) new data for testing (d) blurry, unclear photos
40. 40.🏷️ 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

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