Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 460 · 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 “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
2. 2.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) checking labels twice (d) photos in many lights
3. 3.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
4. 4.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
5. 5.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
6. 6.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
7. 7.True or false: “testing on training data” is bad for training a model. (a) True (b) False
8. 8.🔍 Which is the odd one out? (a) testing on training data (b) labels added at random (c) wrong labels (d) correct labels
9. 9.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the price (d) the font size
10. 10.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) only one kind of example (d) labels added at random
11. 11.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) photos in many lights (d) testing on training data
12. 12.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) clear, sharp examples (d) many varied examples
13. 13.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
14. 14.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
15. 15.True or false: “testing on training data” is good for training a model. (a) True (b) False
16. 16.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
18. 18.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
20. 20.True or false: “photos in many lights” is good for training a model. (a) False (b) True
21. 21.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) testing on training data (d) clear, sharp examples
22. 22.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
23. 23.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
24. 24.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
25. 25.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
26. 26.Which of these is bad for training a model? (a) new data for testing (b) copies of one photo (c) correct labels (d) examples of every type
27. 27.🏷️ 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
28. 28.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) labels added at random (d) photos in many lights
29. 29.🔍 Which is the odd one out? (a) checking labels twice (b) clear, sharp examples (c) wrong labels (d) photos in many lights
30. 30.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) correct labels (d) very few examples
31. 31.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
33. 33.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) photos in many lights (d) only one kind of example
34. 34.True or false: “wrong labels” is good for training a model. (a) False (b) True
35. 35.True or false: “very few examples” is bad for training a model. (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 bad for training a model? (a) wrong labels (b) examples of every type (c) correct labels (d) many varied examples
38. 38.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) correct labels (d) photos in many lights
39. 39.🥭 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
40. 40.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True

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