Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 180 · 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.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the price (d) the font size
2. 2.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
3. 3.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) clean screens (d) charge faster
4. 4.Which of these is bad for training a model? (a) examples of every type (b) blurry, unclear photos (c) many varied examples (d) correct labels
5. 5.True or false: “testing on training data” is good for training a model. (a) True (b) False
6. 6.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
7. 7.🏷️ 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
8. 8.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
9. 9.Which of these is bad for training a model? (a) many varied examples (b) wrong labels (c) clear, sharp examples (d) photos in many lights
10. 10.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
11. 11.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) only one kind of example (d) correct labels
12. 12.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
13. 13.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
14. 14.🏷️ 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
15. 15.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) new data for testing (d) labels added at random
16. 16.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
17. 17.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
18. 18.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) new data for testing (b) testing on training data (c) very few examples (d) wrong labels
20. 20.📚 Usually, more good examples make a model… (a) slower to switch on (b) better at its task (c) forget everything (d) change colour
21. 21.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) examples of every type (d) very few examples
22. 22.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
23. 23.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
24. 24.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
25. 25.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will be perfect (d) it will turn yellow
26. 26.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
27. 27.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
28. 28.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
29. 29.True or false: “examples of every type” is bad for training a model. (a) True (b) False
30. 30.🔍 Which is the odd one out? (a) many varied examples (b) only one kind of example (c) testing on training data (d) very few examples
31. 31.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) testing on training data (d) photos in many lights
32. 32.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
33. 33.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
34. 34.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) correct labels (d) only one kind of example
35. 35.🏷️ 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
36. 36.🔍 Which is the odd one out? (a) testing on training data (b) correct labels (c) clear, sharp examples (d) examples of every type
37. 37.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) correct labels (d) clear, sharp examples
38. 38.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) checking labels twice (d) clear, sharp examples
39. 39.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
40. 40.🏷️ 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

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