Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 178 · 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.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
2. 2.True or false: “many varied examples” is bad for training a model. (a) False (b) True
3. 3.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
4. 4.🏷️ 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
5. 5.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
6. 6.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
7. 7.🏷️ 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
8. 8.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) charge faster (d) clean screens
9. 9.📚 Usually, more good examples make a model… (a) slower to switch on (b) better at its task (c) forget everything (d) change colour
10. 10.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
11. 11.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
12. 12.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) testing on training data (d) checking labels twice
13. 13.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
14. 14.🏷️ 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
15. 15.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
16. 16.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
17. 17.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) very few examples (d) testing on training data
18. 18.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) blurry, unclear photos (d) testing on training data
20. 20.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
21. 21.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
22. 22.🏷️ 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
23. 23.True or false: “copies of one photo” is good for training a model. (a) True (b) False
24. 24.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
25. 25.True or false: “examples of every type” is bad for training a model. (a) True (b) False
26. 26.Which of these is bad for training a model? (a) correct labels (b) testing on training data (c) many varied examples (d) clear, sharp examples
27. 27.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
28. 28.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) the font size (d) a sticker on a laptop
29. 29.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
30. 30.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) testing on training data (d) many varied examples
31. 31.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) clear, sharp examples (d) correct labels
32. 32.Which of these is good for training a model? (a) copies of one photo (b) photos in many lights (c) wrong labels (d) blurry, unclear photos
33. 33.True or false: “photos in many lights” is good for training a model. (a) False (b) True
34. 34.🏷️ 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
35. 35.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) new data for testing (b) very few examples (c) examples of every type (d) clear, sharp examples
37. 37.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
38. 38.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
39. 39.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) checking labels twice (d) copies of one photo
40. 40.True or false: “wrong labels” is good for training a model. (a) True (b) False

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