Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 462 · 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 “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
2. 2.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
3. 3.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
4. 4.Which of these is good for training a model? (a) wrong labels (b) photos in many lights (c) only one kind of example (d) very few examples
5. 5.Which of these is bad for training a model? (a) many varied examples (b) blurry, unclear photos (c) clear, sharp examples (d) photos in many lights
6. 6.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
7. 7.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
8. 8.True or false: “labels added at random” is good for training a model. (a) False (b) True
9. 9.Which of these is good for training a model? (a) very few examples (b) many varied examples (c) copies of one photo (d) only one kind of example
10. 10.🔍 Which is the odd one out? (a) many varied examples (b) testing on training data (c) copies of one photo (d) very few examples
11. 11.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
12. 12.True or false: “photos in many lights” is good for training a model. (a) True (b) False
13. 13.Which of these is good for training a model? (a) labels added at random (b) correct labels (c) testing on training data (d) wrong labels
14. 14.True or false: “wrong labels” is bad for training a model. (a) False (b) True
15. 15.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
16. 16.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
17. 17.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
18. 18.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
19. 19.🔍 Which is the odd one out? (a) clear, sharp examples (b) only one kind of example (c) wrong labels (d) labels added at random
20. 20.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will turn yellow (c) it may get it wrong (d) it will shut down
21. 21.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a list of rules only (d) a song about dogs
22. 22.🔍 Which is the odd one out? (a) copies of one photo (b) labels added at random (c) only one kind of example (d) photos in many lights
23. 23.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) a sticker on a laptop (d) the price
24. 24.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) testing on training data (d) examples of every type
25. 25.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) examples of every type (d) copies of one photo
26. 26.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
27. 27.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
28. 28.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) charge faster (d) print pages
29. 29.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
30. 30.🔍 Which is the odd one out? (a) many varied examples (b) testing on training data (c) clear, sharp examples (d) new data for testing
31. 31.🏷️ 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
32. 32.True or false: “new data for testing” is bad for training a model. (a) False (b) True
33. 33.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
34. 34.True or false: “many varied examples” is bad for training a model. (a) False (b) True
35. 35.🏷️ 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
36. 36.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
37. 37.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
38. 38.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) blurry, unclear photos (d) very few examples
39. 39.🏷️ 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
40. 40.Which of these is good for training a model? (a) checking labels twice (b) testing on training data (c) only one kind of example (d) copies of one photo

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