Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 300 · 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: labels added at random? (a) bad for training (b) good for training
2. 2.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) only one kind of example (d) new data for testing
3. 3.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
4. 4.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) slower to switch on (d) forget everything
5. 5.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
6. 6.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
7. 7.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
8. 8.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) a sticker on a laptop (d) the right answer tag
9. 9.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a song about dogs (d) a list of rules only
11. 11.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
12. 12.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
13. 13.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
14. 14.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
15. 15.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) clear, sharp examples (d) wrong labels
16. 16.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
17. 17.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) testing on training data (d) new data for testing
18. 18.True or false: “copies of one photo” is good for training a model. (a) True (b) False
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
20. 20.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
21. 21.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) labels added at random (d) correct labels
22. 22.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) clear, sharp examples (d) many varied examples
23. 23.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
24. 24.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
25. 25.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
26. 26.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) clear, sharp examples (d) copies of one photo
27. 27.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
28. 28.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) many varied examples (d) checking labels twice
30. 30.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) copies of one photo (d) photos in many lights
31. 31.🔍 Which is the odd one out? (a) checking labels twice (b) blurry, unclear photos (c) many varied examples (d) examples of every type
32. 32.True or false: “examples of every type” is bad for training a model. (a) False (b) True
33. 33.🏷️ 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
34. 34.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) blurry, unclear photos (d) very few examples
35. 35.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) checking labels twice (d) copies of one photo
36. 36.🔍 Which is the odd one out? (a) many varied examples (b) wrong labels (c) correct labels (d) new data for testing
37. 37.True or false: “very few examples” is good for training a model. (a) True (b) False
38. 38.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) examples of every type (d) labels added at random
39. 39.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
40. 40.True or false: “new data for testing” is good for training a model. (a) True (b) False

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