Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 554 · 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? “Happy birthday! See you at lunch” (a) not spam (b) spam
2. 2.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) only one kind of example (d) examples of every type
3. 3.Which of these is bad for training a model? (a) clear, sharp examples (b) labels added at random (c) checking labels twice (d) examples of every type
4. 4.True or false: “new data for testing” is bad for training a model. (a) True (b) False
5. 5.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
6. 6.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
7. 7.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) a song about dogs (d) many labelled photos
9. 9.🏷️ 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
10. 10.🥭 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 may get it wrong (d) it will turn yellow
11. 11.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
12. 12.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
13. 13.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) wrong labels (d) clear, sharp examples
14. 14.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) blurry, unclear photos (d) photos in many lights
15. 15.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
16. 16.Which of these is good for training a model? (a) testing on training data (b) correct labels (c) copies of one photo (d) only one kind of example
17. 17.🏷️ 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
18. 18.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) print pages (d) charge faster
19. 19.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
20. 20.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) checking labels twice (d) only one kind of example
21. 21.True or false: “copies of one photo” is good for training a model. (a) False (b) True
22. 22.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
24. 24.🏷️ 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
25. 25.True or false: “wrong labels” is good for training a model. (a) True (b) False
26. 26.🏷️ 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
27. 27.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
28. 28.True or false: “wrong labels” is bad for training a model. (a) True (b) False
29. 29.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
30. 30.Which of these is bad for training a model? (a) new data for testing (b) very few examples (c) many varied examples (d) examples of every type
31. 31.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) copies of one photo (d) correct labels
32. 32.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) many varied examples (d) labels added at random
33. 33.True or false: “only one kind of example” is good for training a model. (a) False (b) True
34. 34.Which of these is good for training a model? (a) examples of every type (b) only one kind of example (c) testing on training data (d) blurry, unclear photos
35. 35.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
36. 36.Which of these is good for training a model? (a) clear, sharp examples (b) copies of one photo (c) labels added at random (d) very few examples
37. 37.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) checking labels twice (d) copies of one photo
38. 38.🔍 Which is the odd one out? (a) checking labels twice (b) wrong labels (c) correct labels (d) many varied examples
39. 39.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
40. 40.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False

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