Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 143 · 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) examples of every type (b) many varied examples (c) very few examples (d) clear, sharp examples
2. 2.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
3. 3.🏷️ 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
4. 4.📚 Good or bad for training a model: labels added at random? (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? “You won a free phone! Click now!” (a) not spam (b) spam
6. 6.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
7. 7.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) labels added at random (d) examples of every type
8. 8.Which of these is good for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) testing on training data (d) labels added at random
9. 9.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
10. 10.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
11. 11.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
12. 12.True or false: “labels added at random” is good for training a model. (a) False (b) True
13. 13.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
14. 14.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) photos in many lights (d) testing on training data
15. 15.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
16. 16.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will be perfect (d) it will shut down
17. 17.🔍 Which is the odd one out? (a) testing on training data (b) labels added at random (c) very few examples (d) checking labels twice
18. 18.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
19. 19.Which of these is bad for training a model? (a) wrong labels (b) photos in many lights (c) many varied examples (d) checking labels twice
20. 20.🏷️ 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) not spam (b) spam
21. 21.Which of these is bad for training a model? (a) only one kind of example (b) photos in many lights (c) examples of every type (d) many varied examples
22. 22.🏷️ 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
23. 23.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
24. 24.Which of these is bad for training a model? (a) very few examples (b) examples of every type (c) correct labels (d) checking labels twice
25. 25.🔍 Which is the odd one out? (a) wrong labels (b) photos in many lights (c) new data for testing (d) clear, sharp examples
26. 26.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
27. 27.Which of these is good for training a model? (a) copies of one photo (b) many varied examples (c) wrong labels (d) only one kind of example
28. 28.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
29. 29.🏷️ 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
30. 30.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
31. 31.True or false: “examples of every type” is good for training a model. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) many varied examples (d) testing on training data
33. 33.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
34. 34.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
35. 35.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
36. 36.🏷️ 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
37. 37.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
38. 38.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
39. 39.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) new data for testing (d) blurry, unclear photos
40. 40.True or false: “correct labels” is good for training a model. (a) False (b) True

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