Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 334 · 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 of these is good for training a model? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) checking labels twice
2. 2.🏷️ 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
3. 3.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
4. 4.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the right answer tag (d) the font size
5. 5.Which of these is bad for training a model? (a) new data for testing (b) checking labels twice (c) wrong labels (d) correct labels
6. 6.Which of these is bad for training a model? (a) checking labels twice (b) examples of every type (c) labels added at random (d) new data for testing
7. 7.🔍 Which is the odd one out? (a) photos in many lights (b) wrong labels (c) many varied examples (d) checking labels twice
8. 8.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
9. 9.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
10. 10.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
11. 11.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
12. 12.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
13. 13.True or false: “very few examples” is bad for training a model. (a) False (b) True
14. 14.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
15. 15.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
16. 16.🏷️ 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
17. 17.🔍 Which is the odd one out? (a) only one kind of example (b) new data for testing (c) checking labels twice (d) many varied examples
18. 18.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) photos in many lights (d) testing on training data
19. 19.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) blurry, unclear photos (d) new data for testing
20. 20.Which of these is good for training a model? (a) copies of one photo (b) wrong labels (c) many varied examples (d) only one kind of example
21. 21.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
22. 22.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) blurry, unclear photos (d) correct labels
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
24. 24.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) photos in many lights (d) very few examples
25. 25.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it may get it wrong (d) it will be perfect
26. 26.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
27. 27.Which of these is good for training a model? (a) correct labels (b) very few examples (c) only one kind of example (d) labels added at random
28. 28.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) examples of every type (d) copies of one photo
29. 29.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) labels added at random (d) many varied examples
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
31. 31.True or false: “labels added at random” is bad for training a model. (a) True (b) False
32. 32.Which of these is bad for training a model? (a) testing on training data (b) clear, sharp examples (c) photos in many lights (d) correct labels
33. 33.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) photos in many lights (d) very few examples
34. 34.🏷️ 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
35. 35.🏷️ 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
36. 36.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
38. 38.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
39. 39.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
40. 40.True or false: “checking labels twice” is bad for training a model. (a) True (b) False

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