Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 105 · 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: “very few examples” is bad for training a model. (a) False (b) True
2. 2.🏷️ 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
3. 3.True or false: “examples of every type” is bad for training a model. (a) True (b) False
4. 4.🏷️ 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
5. 5.🔍 Which is the odd one out? (a) correct labels (b) wrong labels (c) photos in many lights (d) checking labels twice
6. 6.True or false: “photos in many lights” is good for training a model. (a) True (b) False
7. 7.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
8. 8.🔍 Which is the odd one out? (a) labels added at random (b) examples of every type (c) correct labels (d) clear, sharp examples
9. 9.Which of these is bad for training a model? (a) labels added at random (b) examples of every type (c) clear, sharp examples (d) photos in many lights
10. 10.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
11. 11.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) clear, sharp examples (d) copies of one photo
13. 13.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
14. 14.Which of these is good for training a model? (a) photos in many lights (b) labels added at random (c) copies of one photo (d) blurry, unclear photos
15. 15.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) new data for testing (d) wrong labels
16. 16.🏷️ 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
17. 17.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
18. 18.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
19. 19.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
20. 20.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) labels added at random (d) examples of every type
21. 21.🥭 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 shut down (d) it will be perfect
22. 22.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) correct labels (d) testing on training data
23. 23.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
24. 24.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) a sticker on a laptop (d) the font size
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.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) change colour (d) slower to switch on
27. 27.Which of these is good for training a model? (a) clear, sharp examples (b) testing on training data (c) wrong labels (d) copies of one photo
28. 28.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
29. 29.Which of these is bad for training a model? (a) checking labels twice (b) wrong labels (c) correct labels (d) new data for testing
30. 30.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
31. 31.True or false: “many varied examples” is bad for training a model. (a) False (b) True
32. 32.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
33. 33.True or false: “wrong labels” is bad for training a model. (a) True (b) False
34. 34.Which of these is bad for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) many varied examples (d) new data for testing
35. 35.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
36. 36.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
37. 37.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
38. 38.True or false: “correct labels” is bad for training a model. (a) False (b) True
39. 39.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) very few examples (d) photos in many lights
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam

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