Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 580 · 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.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) correct labels (d) wrong labels
2. 2.True or false: “many varied examples” is good for training a model. (a) False (b) True
3. 3.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
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.🔍 Which is the odd one out? (a) photos in many lights (b) correct labels (c) wrong labels (d) clear, sharp examples
6. 6.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will be perfect (c) it will shut down (d) it may get it wrong
7. 7.True or false: “testing on training data” is good for training a model. (a) False (b) True
8. 8.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) copies of one photo (d) many varied examples
9. 9.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
10. 10.🔍 Which is the odd one out? (a) photos in many lights (b) copies of one photo (c) wrong labels (d) only one kind of example
11. 11.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) copies of one photo (d) new data for testing
12. 12.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) clear, sharp examples (d) testing on training data
13. 13.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) photos in many lights (d) testing on training data
14. 14.Which of these is bad for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) correct labels (d) examples of every type
15. 15.Which of these is good for training a model? (a) examples of every type (b) testing on training data (c) blurry, unclear photos (d) very few examples
16. 16.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
17. 17.True or false: “new data for testing” is good for training a model. (a) False (b) True
18. 18.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
19. 19.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
20. 20.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
21. 21.Which of these is good for training a model? (a) copies of one photo (b) new data for testing (c) very few examples (d) wrong labels
22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
23. 23.🏷️ 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
24. 24.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
25. 25.🏷️ 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
26. 26.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) the price (d) a sticker on a laptop
27. 27.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
28. 28.Which of these is bad for training a model? (a) examples of every type (b) only one kind of example (c) new data for testing (d) photos in many lights
29. 29.🔍 Which is the odd one out? (a) very few examples (b) wrong labels (c) only one kind of example (d) correct labels
30. 30.🏷️ 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
31. 31.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
32. 32.🏷️ 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
33. 33.True or false: “examples of every type” 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) True (b) False
35. 35.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
36. 36.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) one single photo (d) many labelled photos
37. 37.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
38. 38.🔍 Which is the odd one out? (a) clear, sharp examples (b) very few examples (c) blurry, unclear photos (d) wrong labels
39. 39.True or false: “very few examples” is good for training a model. (a) True (b) False
40. 40.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training

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