Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 499 · 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: examples of every type? (a) bad for training (b) good for training
2. 2.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
3. 3.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
4. 4.🏷️ 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
5. 5.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
6. 6.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
7. 7.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
8. 8.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
9. 9.True or false: “new data for testing” is good for training a model. (a) False (b) True
10. 10.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
11. 11.Which of these is good for training a model? (a) only one kind of example (b) examples of every type (c) very few examples (d) blurry, unclear photos
12. 12.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
13. 13.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) very few examples (d) clear, sharp examples
14. 14.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) examples of every type (d) wrong labels
15. 15.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) wrong labels (d) clear, sharp examples
16. 16.True or false: “labels added at random” is good for training a model. (a) False (b) True
17. 17.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
18. 18.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
19. 19.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
20. 20.Which of these is good for training a model? (a) many varied examples (b) copies of one photo (c) blurry, unclear photos (d) very few examples
21. 21.🏷️ 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
22. 22.Which of these is bad for training a model? (a) checking labels twice (b) many varied examples (c) photos in many lights (d) blurry, unclear photos
23. 23.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
24. 24.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
26. 26.Which of these is good for training a model? (a) labels added at random (b) checking labels twice (c) testing on training data (d) very few examples
27. 27.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
28. 28.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it may get it wrong (c) it will shut down (d) it will be perfect
29. 29.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) photos in many lights (d) very few examples
30. 30.Which of these is bad for training a model? (a) many varied examples (b) wrong labels (c) examples of every type (d) checking labels twice
31. 31.True or false: “very few examples” is good for training a model. (a) True (b) False
32. 32.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
33. 33.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the font size (d) the right answer tag
34. 34.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
35. 35.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
36. 36.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) only one kind of example (d) examples of every type
37. 37.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) testing on training data (b) many varied examples (c) copies of one photo (d) only one kind of example
39. 39.Which of these is good for training a model? (a) very few examples (b) clear, sharp examples (c) testing on training data (d) wrong labels
40. 40.True or false: “only one kind of example” is good for training a model. (a) False (b) True

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