Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 197 · 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.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
2. 2.True or false: “examples of every type” is bad for training a model. (a) True (b) False
3. 3.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
4. 4.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
5. 5.True or false: “very few examples” is bad for training a model. (a) False (b) True
6. 6.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
7. 7.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) new data for testing (d) correct labels
8. 8.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
9. 9.Which of these is bad for training a model? (a) clear, sharp examples (b) correct labels (c) examples of every type (d) wrong labels
10. 10.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
11. 11.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) correct labels (b) checking labels twice (c) only one kind of example (d) many varied examples
13. 13.Which of these is bad for training a model? (a) checking labels twice (b) new data for testing (c) copies of one photo (d) correct labels
14. 14.🥭 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
15. 15.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) labels added at random (d) testing on training data
16. 16.🏷️ 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
17. 17.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) many varied examples (d) clear, sharp examples
18. 18.🏷️ 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
19. 19.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the price (d) the right answer tag
20. 20.🏷️ 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
21. 21.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
22. 22.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
23. 23.🔍 Which is the odd one out? (a) blurry, unclear photos (b) labels added at random (c) new data for testing (d) only one kind of example
24. 24.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
25. 25.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
26. 26.Which of these is bad for training a model? (a) clear, sharp examples (b) examples of every type (c) very few examples (d) checking labels twice
27. 27.True or false: “copies of one photo” is good for training a model. (a) False (b) True
28. 28.True or false: “new data for testing” is bad for training a model. (a) False (b) True
29. 29.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
30. 30.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) blurry, unclear photos (d) new data for testing
31. 31.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) blurry, unclear photos (d) checking labels twice
32. 32.🏷️ 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
33. 33.📚 Good or bad for training a model: checking labels twice? (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) False (b) True
35. 35.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
36. 36.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
37. 37.True or false: “labels added at random” is good for training a model. (a) True (b) False
38. 38.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
39. 39.True or false: “wrong labels” is bad for training a model. (a) True (b) False
40. 40.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) blurry, unclear photos (d) clear, sharp examples

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