Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 379 · 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 bad for training a model? (a) photos in many lights (b) very few examples (c) correct labels (d) examples of every type
2. 2.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) only one kind of example (d) checking labels twice
3. 3.🏷️ 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
4. 4.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
5. 5.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
6. 6.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) copies of one photo (d) checking labels twice
7. 7.🏷️ 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
8. 8.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
9. 9.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
10. 10.🏷️ 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
11. 11.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) correct labels (d) labels added at random
12. 12.True or false: “labels added at random” is good for training a model. (a) False (b) True
13. 13.🥭 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
14. 14.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) correct labels (d) labels added at random
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
16. 16.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
17. 17.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) many labelled photos (d) a song about dogs
18. 18.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) photos in many lights (d) blurry, unclear photos
19. 19.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
20. 20.🏷️ 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
21. 21.🏷️ 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
22. 22.🔍 Which is the odd one out? (a) copies of one photo (b) clear, sharp examples (c) only one kind of example (d) labels added at random
23. 23.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) new data for testing (d) very few examples
24. 24.🔍 Which is the odd one out? (a) blurry, unclear photos (b) examples of every type (c) very few examples (d) copies of one photo
25. 25.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
26. 26.🏷️ 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
27. 27.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) correct labels (d) clear, sharp examples
28. 28.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
29. 29.Which of these is bad for training a model? (a) photos in many lights (b) checking labels twice (c) many varied examples (d) wrong labels
30. 30.Which of these is good for training a model? (a) blurry, unclear photos (b) labels added at random (c) copies of one photo (d) examples of every type
31. 31.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
32. 32.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
33. 33.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
34. 34.True or false: “checking labels twice” is good for training a model. (a) True (b) False
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 good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) testing on training data (d) many varied examples
37. 37.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
38. 38.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
39. 39.True or false: “testing on training data” is good for training a model. (a) False (b) True
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam

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