Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 70 · 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.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) many varied examples (d) blurry, unclear photos
2. 2.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
3. 3.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) testing on training data (d) clear, sharp examples
4. 4.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
5. 5.🏷️ 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
6. 6.🏷️ 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
7. 7.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
8. 8.Which of these is bad for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) correct labels (d) photos in many lights
9. 9.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
10. 10.🏷️ 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
11. 11.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
12. 12.True or false: “wrong labels” is good for training a model. (a) True (b) False
13. 13.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
14. 14.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
15. 15.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
16. 16.🔍 Which is the odd one out? (a) checking labels twice (b) testing on training data (c) examples of every type (d) many varied examples
17. 17.True or false: “new data for testing” is good for training a model. (a) True (b) False
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
19. 19.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) correct labels (d) blurry, unclear photos
20. 20.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
21. 21.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will turn yellow (c) it will shut down (d) it may get it wrong
22. 22.🏷️ 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
23. 23.True or false: “labels added at random” is bad for training a model. (a) True (b) False
24. 24.Which of these is bad for training a model? (a) examples of every type (b) copies of one photo (c) clear, sharp examples (d) correct labels
25. 25.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) only one kind of example (d) clear, sharp examples
26. 26.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
27. 27.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) clear, sharp examples (d) correct labels
28. 28.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) slower to switch on (d) better at its task
29. 29.🏷️ 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
30. 30.🏷️ 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
31. 31.True or false: “examples of every type” is bad for training a model. (a) False (b) True
32. 32.🏷️ 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
33. 33.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
34. 34.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) clear, sharp examples (d) examples of every type
35. 35.🔍 Which is the odd one out? (a) clear, sharp examples (b) photos in many lights (c) correct labels (d) labels added at random
36. 36.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
37. 37.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
38. 38.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
39. 39.True or false: “many varied examples” is bad for training a model. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) examples of every type (d) wrong labels

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