Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 68 · 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 “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
2. 2.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) photos in many lights
3. 3.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
4. 4.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
5. 5.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) checking labels twice (d) blurry, unclear photos
6. 6.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) very few examples (d) clear, sharp examples
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
9. 9.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
10. 10.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
11. 11.Which of these is good for training a model? (a) wrong labels (b) checking labels twice (c) blurry, unclear photos (d) very few examples
12. 12.🔍 Which is the odd one out? (a) wrong labels (b) copies of one photo (c) very few examples (d) photos in many lights
13. 13.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) correct labels (d) very few examples
14. 14.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
15. 15.📚 Usually, more good examples make a model… (a) better at its task (b) slower to switch on (c) change colour (d) forget everything
16. 16.🔍 Which is the odd one out? (a) blurry, unclear photos (b) many varied examples (c) labels added at random (d) testing on training data
17. 17.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
18. 18.True or false: “only one kind of example” is good for training a model. (a) False (b) True
19. 19.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) a list of rules only (d) one single photo
20. 20.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) examples of every type (d) testing on training data
21. 21.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
23. 23.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
25. 25.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
26. 26.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the font size (d) the right answer tag
27. 27.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) copies of one photo (d) checking labels twice
28. 28.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) examples of every type (d) only one kind of example
29. 29.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) examples of every type (d) blurry, unclear photos
30. 30.Which of these is good for training a model? (a) testing on training data (b) clear, sharp examples (c) only one kind of example (d) blurry, unclear photos
31. 31.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
32. 32.🏷️ 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
33. 33.True or false: “many varied examples” is good for training a model. (a) True (b) False
34. 34.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
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) spam (b) not spam
36. 36.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) print pages (d) clean screens
37. 37.🏷️ 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
38. 38.True or false: “examples of every type” is good for training a model. (a) False (b) True
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.True or false: “photos in many lights” is good for training a model. (a) True (b) False

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