Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 250 · 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: “only one kind of example” is good for training a model. (a) True (b) False
2. 2.🔍 Which is the odd one out? (a) wrong labels (b) checking labels twice (c) labels added at random (d) blurry, unclear photos
3. 3.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
4. 4.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
5. 5.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
6. 6.🏷️ 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
7. 7.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) copies of one photo (d) many varied examples
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
9. 9.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
10. 10.Which of these is bad for training a model? (a) blurry, unclear photos (b) photos in many lights (c) checking labels twice (d) new data for testing
11. 11.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
12. 12.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) many varied examples
13. 13.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
14. 14.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
15. 15.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) examples of every type (d) wrong labels
16. 16.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) wrong labels (d) copies of one photo
17. 17.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
18. 18.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
19. 19.True or false: “correct labels” is good for training a model. (a) False (b) True
20. 20.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
21. 21.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) clean screens (d) print pages
22. 22.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) forget everything (d) better at its task
23. 23.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) many varied examples (d) wrong labels
24. 24.🔍 Which is the odd one out? (a) correct labels (b) clear, sharp examples (c) examples of every type (d) very few examples
25. 25.Which of these is good for training a model? (a) correct labels (b) labels added at random (c) very few examples (d) wrong labels
26. 26.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
27. 27.🏷️ 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
28. 28.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) wrong labels (b) photos in many lights (c) copies of one photo (d) blurry, unclear photos
30. 30.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
31. 31.🏷️ 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
32. 32.True or false: “labels added at random” is bad for training a model. (a) False (b) True
33. 33.🥭 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 be perfect (d) it will shut down
34. 34.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) very few examples (d) checking labels twice
35. 35.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
36. 36.True or false: “correct labels” is bad for training a model. (a) True (b) False
37. 37.Which of these is good for training a model? (a) only one kind of example (b) labels added at random (c) clear, sharp examples (d) blurry, unclear photos
38. 38.🏷️ 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
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (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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