Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 557 · 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.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
2. 2.Which of these is bad for training a model? (a) correct labels (b) blurry, unclear photos (c) new data for testing (d) clear, sharp examples
3. 3.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
4. 4.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
5. 5.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
6. 6.Which of these is bad for training a model? (a) clear, sharp examples (b) checking labels twice (c) only one kind of example (d) new data for testing
7. 7.True or false: “very few examples” is good for training a model. (a) True (b) False
8. 8.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
9. 9.Which of these is bad for training a model? (a) checking labels twice (b) clear, sharp examples (c) examples of every type (d) copies of one photo
10. 10.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) better at its task (d) change colour
11. 11.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
12. 12.🏷️ 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
13. 13.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
14. 14.🏷️ 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
15. 15.Which of these is good for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) copies of one photo (d) wrong labels
16. 16.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
17. 17.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
18. 18.True or false: “only one kind of example” is good for training a model. (a) True (b) False
19. 19.🏷️ 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
20. 20.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
21. 21.Which of these is good for training a model? (a) blurry, unclear photos (b) labels added at random (c) wrong labels (d) checking labels twice
22. 22.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
23. 23.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it will be perfect (d) it may get it wrong
24. 24.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
25. 25.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) examples of every type (d) only one kind of example
27. 27.🏷️ 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
28. 28.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
29. 29.Which of these is good for training a model? (a) many varied examples (b) blurry, unclear photos (c) wrong labels (d) copies of one photo
30. 30.Which of these is good for training a model? (a) testing on training data (b) correct labels (c) very few examples (d) copies of one photo
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
32. 32.Which of these is bad for training a model? (a) labels added at random (b) correct labels (c) new data for testing (d) clear, sharp examples
33. 33.🔍 Which is the odd one out? (a) very few examples (b) clear, sharp examples (c) many varied examples (d) new data for testing
34. 34.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
35. 35.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) labels added at random (d) checking labels twice
37. 37.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
38. 38.🏷️ 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
39. 39.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) very few examples (d) photos in many lights
40. 40.True or false: “testing on training data” is good for training a model. (a) False (b) True

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