Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 223 · 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 “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
2. 2.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on
3. 3.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
4. 4.🥭 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
5. 5.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
6. 6.Which of these is bad for training a model? (a) new data for testing (b) clear, sharp examples (c) many varied examples (d) blurry, unclear photos
7. 7.True or false: “copies of one photo” is good for training a model. (a) True (b) False
8. 8.🏷️ 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
9. 9.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
10. 10.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
11. 11.True or false: “examples of every type” is good for training a model. (a) False (b) True
12. 12.True or false: “testing on training data” is good for training a model. (a) False (b) True
13. 13.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) wrong labels (d) examples of every type
14. 14.🏷️ 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
15. 15.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
16. 16.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) photos in many lights (d) testing on training data
17. 17.True or false: “very few examples” is good for training a model. (a) False (b) True
18. 18.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) new data for testing (d) very few examples
19. 19.🔍 Which is the odd one out? (a) blurry, unclear photos (b) correct labels (c) copies of one photo (d) labels added at random
20. 20.Which of these is bad for training a model? (a) examples of every type (b) very few examples (c) correct labels (d) clear, sharp examples
21. 21.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
22. 22.🔍 Which is the odd one out? (a) clear, sharp examples (b) only one kind of example (c) correct labels (d) many varied examples
23. 23.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) blurry, unclear photos (d) new data for testing
24. 24.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
25. 25.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
26. 26.True or false: “correct labels” is good for training a model. (a) True (b) False
27. 27.🔍 Which is the odd one out? (a) correct labels (b) checking labels twice (c) clear, sharp examples (d) very few examples
28. 28.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) learn from data (d) print pages
29. 29.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
30. 30.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
31. 31.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
33. 33.🏷️ 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
34. 34.True or false: “new data for testing” is bad for training a model. (a) True (b) False
35. 35.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) examples of every type (d) copies of one photo
36. 36.True or false: “labels added at random” is good for training a model. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) many varied examples (d) testing on training data
38. 38.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) the font size (d) a sticker on a laptop
39. 39.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) testing on training data (d) checking labels twice
40. 40.True or false: “checking labels twice” is good for training a model. (a) True (b) False

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