Machine Learning for Kids Worksheet

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

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