Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 506 · 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: labels added at random? (a) good for training (b) bad for training
2. 2.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
3. 3.True or false: “new data for testing” is good for training a model. (a) False (b) True
4. 4.Which of these is good for training a model? (a) blurry, unclear photos (b) checking labels twice (c) only one kind of example (d) wrong labels
5. 5.🔍 Which is the odd one out? (a) correct labels (b) only one kind of example (c) new data for testing (d) many varied examples
6. 6.True or false: “many varied examples” is good for training a model. (a) True (b) False
7. 7.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
8. 8.True or false: “only one kind of example” is good for training a model. (a) False (b) True
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) a list of rules only (d) many labelled photos
10. 10.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
11. 11.Which of these is good for training a model? (a) labels added at random (b) examples of every type (c) blurry, unclear photos (d) very few examples
12. 12.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) only one kind of example (d) labels added at random
13. 13.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) checking labels twice (d) labels added at random
14. 14.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
15. 15.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
16. 16.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
17. 17.True or false: “many varied examples” is bad for training a model. (a) True (b) False
18. 18.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
19. 19.True or false: “testing on training data” is good for training a model. (a) False (b) True
20. 20.🔍 Which is the odd one out? (a) examples of every type (b) blurry, unclear photos (c) testing on training data (d) only one kind of example
21. 21.True or false: “correct labels” is bad for training a model. (a) True (b) False
22. 22.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
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) spam (b) not spam
24. 24.🏷️ 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
25. 25.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
26. 26.True or false: “wrong labels” is good for training a model. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) many varied examples (d) checking labels twice
28. 28.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
29. 29.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) wrong labels (d) clear, sharp examples
30. 30.🔍 Which is the odd one out? (a) testing on training data (b) checking labels twice (c) copies of one photo (d) very few examples
31. 31.Which of these is good for training a model? (a) copies of one photo (b) photos in many lights (c) very few examples (d) wrong labels
32. 32.🔍 Which is the odd one out? (a) clear, sharp examples (b) testing on training data (c) many varied examples (d) correct labels
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.Which of these is bad for training a model? (a) very few examples (b) photos in many lights (c) new data for testing (d) checking labels twice
35. 35.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
36. 36.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
37. 37.Which of these is bad for training a model? (a) examples of every type (b) photos in many lights (c) correct labels (d) blurry, unclear photos
38. 38.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it may get it wrong (d) it will turn yellow
39. 39.True or false: “correct labels” is good for training a model. (a) True (b) False
40. 40.🏷️ 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

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