Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 167 · 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: wrong labels? (a) bad for training (b) good for training
2. 2.🔍 Which is the odd one out? (a) only one kind of example (b) photos in many lights (c) blurry, unclear photos (d) very few examples
3. 3.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
5. 5.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
6. 6.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) very few examples (d) correct labels
7. 7.🏷️ 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
8. 8.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
9. 9.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
10. 10.Which of these is bad for training a model? (a) only one kind of example (b) clear, sharp examples (c) new data for testing (d) many varied examples
11. 11.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) clear, sharp examples (d) testing on training data
12. 12.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) only one kind of example (d) photos in many lights
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) new data for testing (d) copies of one photo
14. 14.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) new data for testing (d) testing on training data
15. 15.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
16. 16.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will shut down (d) it will be perfect
17. 17.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
18. 18.🔍 Which is the odd one out? (a) only one kind of example (b) photos in many lights (c) clear, sharp examples (d) checking labels twice
19. 19.Which of these is good for training a model? (a) examples of every type (b) testing on training data (c) only one kind of example (d) blurry, unclear photos
20. 20.🔍 Which is the odd one out? (a) very few examples (b) checking labels twice (c) new data for testing (d) examples of every type
21. 21.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) examples of every type (d) blurry, unclear photos
22. 22.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
23. 23.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
24. 24.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
25. 25.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) only one kind of example (d) checking labels twice
26. 26.True or false: “new data for testing” is bad for training a model. (a) False (b) True
27. 27.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
28. 28.True or false: “correct labels” is bad for training a model. (a) True (b) False
29. 29.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) labels added at random (d) new data for testing
30. 30.🏷️ 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
31. 31.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
32. 32.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
33. 33.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
34. 34.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
35. 35.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) clear, sharp examples (d) very few examples
36. 36.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
37. 37.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) many varied examples (d) very few examples
38. 38.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
39. 39.🏷️ 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
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam

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