Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 486 · 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 “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
2. 2.True or false: “correct labels” is bad for training a model. (a) True (b) False
3. 3.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
4. 4.Which of these is good for training a model? (a) testing on training data (b) many varied examples (c) only one kind of example (d) labels added at random
5. 5.🔍 Which is the odd one out? (a) examples of every type (b) blurry, unclear photos (c) copies of one photo (d) wrong labels
6. 6.True or false: “examples of every type” is bad for training a model. (a) True (b) False
7. 7.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
8. 8.True or false: “many varied examples” is good for training a model. (a) False (b) True
9. 9.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
10. 10.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) labels added at random (d) many varied examples
11. 11.Which of these is bad for training a model? (a) new data for testing (b) copies of one photo (c) many varied examples (d) photos in many lights
12. 12.🔍 Which is the odd one out? (a) clear, sharp examples (b) copies of one photo (c) new data for testing (d) correct labels
13. 13.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
14. 14.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) testing on training data (d) photos in many lights
15. 15.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) many labelled photos (c) a list of rules only (d) one single photo
16. 16.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
17. 17.🏷️ 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
18. 18.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) very few examples (d) correct labels
19. 19.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
20. 20.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) slower to switch on (d) forget everything
21. 21.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
22. 22.Which of these is good for training a model? (a) clear, sharp examples (b) wrong labels (c) testing on training data (d) blurry, unclear photos
23. 23.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
24. 24.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
25. 25.🏷️ 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
26. 26.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
27. 27.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) correct labels (d) very few examples
28. 28.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
29. 29.Which of these is bad for training a model? (a) new data for testing (b) very few examples (c) many varied examples (d) examples of every type
30. 30.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) wrong labels (d) clear, sharp examples
31. 31.🥭 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
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
33. 33.Which of these is bad for training a model? (a) blurry, unclear photos (b) checking labels twice (c) photos in many lights (d) new data for testing
34. 34.True or false: “testing on training data” is bad for training a model. (a) True (b) False
35. 35.🔍 Which is the odd one out? (a) blurry, unclear photos (b) wrong labels (c) testing on training data (d) photos in many lights
36. 36.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
37. 37.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
38. 38.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
39. 39.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
40. 40.True or false: “correct labels” is good for training a model. (a) False (b) True

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