Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 90 · 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 “spam”. (a) False (b) True
2. 2.Which of these is bad for training a model? (a) blurry, unclear photos (b) photos in many lights (c) checking labels twice (d) clear, sharp examples
3. 3.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) copies of one photo (d) correct labels
4. 4.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
5. 5.🏷️ 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
6. 6.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
7. 7.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
8. 8.🥭 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
9. 9.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) wrong labels (d) checking labels twice
10. 10.True or false: “new data for testing” is good for training a model. (a) True (b) False
11. 11.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
12. 12.Which of these is bad for training a model? (a) clear, sharp examples (b) labels added at random (c) examples of every type (d) checking labels twice
13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
14. 14.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) correct labels (d) very few examples
15. 15.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
16. 16.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) many varied examples (d) correct labels
17. 17.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) many varied examples (d) copies of one photo
18. 18.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
19. 19.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
20. 20.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) clear, sharp examples (d) many varied examples
21. 21.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
22. 22.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
23. 23.True or false: “photos in many lights” is good for training a model. (a) True (b) False
24. 24.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
25. 25.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) wrong labels (d) checking labels twice
26. 26.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) only one kind of example (d) clear, sharp examples
27. 27.🏷️ 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
28. 28.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
29. 29.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) learn from data (d) clean screens
30. 30.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) testing on training data (d) correct labels
31. 31.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) new data for testing (d) copies of one photo
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
33. 33.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) wrong labels (d) checking labels twice
34. 34.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
35. 35.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
36. 36.True or false: “labels added at random” is bad for training a model. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) new data for testing (b) testing on training data (c) blurry, unclear photos (d) copies of one photo
38. 38.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) labels added at random (d) photos in many lights
39. 39.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
40. 40.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training

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