Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 314 · 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.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) only one kind of example (d) photos in many lights
2. 2.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) examples of every type (d) wrong labels
3. 3.🔍 Which is the odd one out? (a) checking labels twice (b) only one kind of example (c) blurry, unclear photos (d) copies of one photo
4. 4.True or false: “new data for testing” is bad for training a model. (a) True (b) False
5. 5.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) many varied examples (d) wrong labels
6. 6.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
7. 7.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
8. 8.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
9. 9.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
10. 10.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
11. 11.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
12. 12.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
13. 13.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it may get it wrong (d) it will turn yellow
14. 14.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
15. 15.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) very few examples (d) new data for testing
16. 16.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) labels added at random (d) blurry, unclear photos
17. 17.🏷️ 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
18. 18.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) photos in many lights (d) wrong labels
19. 19.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
21. 21.True or false: “wrong labels” is good for training a model. (a) True (b) False
22. 22.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on
23. 23.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
24. 24.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
25. 25.Which of these is bad for training a model? (a) many varied examples (b) clear, sharp examples (c) only one kind of example (d) correct labels
26. 26.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) photos in many lights (d) testing on training data
27. 27.True or false: “many varied examples” is good for training a model. (a) False (b) True
28. 28.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
29. 29.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) very few examples (d) many varied examples
30. 30.🏷️ 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
31. 31.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) correct labels (d) blurry, unclear photos
32. 32.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
33. 33.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
34. 34.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
35. 35.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) testing on training data (b) examples of every type (c) blurry, unclear photos (d) labels added at random
37. 37.🔍 Which is the odd one out? (a) blurry, unclear photos (b) new data for testing (c) testing on training data (d) only one kind of example
38. 38.Which of these is bad for training a model? (a) examples of every type (b) photos in many lights (c) clear, sharp examples (d) testing on training data
39. 39.True or false: “copies of one photo” is good for training a model. (a) True (b) False
40. 40.Which of these is bad for training a model? (a) clear, sharp examples (b) checking labels twice (c) copies of one photo (d) photos in many lights

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