Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 171 · 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.True or false: “examples of every type” is bad for training a model. (a) False (b) True
3. 3.🏷️ 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
4. 4.🥭 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
5. 5.True or false: “only one kind of example” is good for training a model. (a) False (b) True
6. 6.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
7. 7.🔍 Which is the odd one out? (a) correct labels (b) examples of every type (c) many varied examples (d) copies of one photo
8. 8.Which of these is bad for training a model? (a) examples of every type (b) new data for testing (c) labels added at random (d) checking labels twice
9. 9.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) new data for testing (d) testing on training data
10. 10.Which of these is bad for training a model? (a) correct labels (b) testing on training data (c) new data for testing (d) checking labels twice
11. 11.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) testing on training data (b) photos in many lights (c) examples of every type (d) correct labels
13. 13.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
14. 14.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) slower to switch on (d) change colour
15. 15.True or false: “examples of every type” is good for training a model. (a) True (b) False
16. 16.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) new data for testing (d) only one kind of example
17. 17.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
18. 18.True or false: “checking labels twice” is good for training a model. (a) False (b) True
19. 19.True or false: “labels added at random” is bad for training a model. (a) True (b) False
20. 20.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) clear, sharp examples (d) very few examples
21. 21.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
22. 22.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) many labelled photos (d) a list of rules only
23. 23.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) clean screens (d) charge faster
24. 24.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) correct labels (d) wrong labels
25. 25.🏷️ 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
26. 26.🏷️ 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
27. 27.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
28. 28.Which of these is good for training a model? (a) photos in many lights (b) copies of one photo (c) labels added at random (d) blurry, unclear photos
29. 29.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) very few examples (d) clear, sharp 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) not spam (b) spam
31. 31.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
32. 32.🔍 Which is the odd one out? (a) only one kind of example (b) wrong labels (c) many varied examples (d) very few examples
33. 33.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
34. 34.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) only one kind of example (d) checking labels twice
35. 35.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
36. 36.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
37. 37.🏷️ 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
38. 38.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
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.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training

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