Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 256 · 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 “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
2. 2.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) copies of one photo (d) many varied examples
3. 3.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
5. 5.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) blurry, unclear photos (d) new data for testing
6. 6.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
7. 7.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
8. 8.🔍 Which is the odd one out? (a) very few examples (b) correct labels (c) copies of one photo (d) testing on training data
9. 9.True or false: “testing on training data” is bad for training a model. (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) many varied examples (d) blurry, unclear photos
11. 11.🔍 Which is the odd one out? (a) testing on training data (b) labels added at random (c) photos in many lights (d) wrong labels
12. 12.True or false: “labels added at random” is good for training a model. (a) False (b) True
13. 13.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
14. 14.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) not spam (b) spam
15. 15.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
16. 16.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the right answer tag (d) the font size
17. 17.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it may get it wrong (d) it will be perfect
18. 18.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) clear, sharp examples (d) labels added at random
19. 19.Which of these is bad for training a model? (a) very few examples (b) checking labels twice (c) examples of every type (d) photos in many lights
20. 20.Which of these is bad for training a model? (a) clear, sharp examples (b) examples of every type (c) copies of one photo (d) correct labels
21. 21.Which of these is good for training a model? (a) wrong labels (b) labels added at random (c) correct labels (d) very few examples
22. 22.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
23. 23.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
24. 24.🏷️ 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
25. 25.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
26. 26.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
27. 27.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
28. 28.🏷️ 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
29. 29.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
30. 30.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) only one kind of example (d) new data for testing
31. 31.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) change colour (d) slower to switch on
32. 32.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) photos in many lights (d) wrong labels
33. 33.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
34. 34.True or false: “many varied examples” is bad for training a model. (a) True (b) False
35. 35.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) labels added at random (d) checking labels twice
36. 36.🏷️ 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
37. 37.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
38. 38.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) wrong labels (d) examples of every type
39. 39.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
40. 40.True or false: “correct labels” is bad for training a model. (a) False (b) True

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