Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 588 · 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: “new data for testing” is good for training a model. (a) False (b) True
2. 2.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) testing on training data (d) new data for testing
3. 3.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
4. 4.True or false: “testing on training data” is bad for training a model. (a) False (b) True
5. 5.🔍 Which is the odd one out? (a) correct labels (b) examples of every type (c) copies of one photo (d) checking labels twice
6. 6.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
7. 7.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) photos in many lights (d) blurry, unclear photos
8. 8.🏷️ 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
9. 9.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) only one kind of example (d) photos in many lights
10. 10.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) correct labels (d) wrong labels
11. 11.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will be perfect (d) it will shut down
12. 12.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
13. 13.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
14. 14.🏷️ 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
15. 15.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
16. 16.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) new data for testing (d) very few examples
17. 17.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
18. 18.🏷️ 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
19. 19.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) blurry, unclear photos (d) checking labels twice
20. 20.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
21. 21.Which of these is good for training a model? (a) wrong labels (b) correct labels (c) blurry, unclear photos (d) only one kind of example
22. 22.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) labels added at random (d) clear, sharp examples
23. 23.True or false: “new data for testing” is bad for training a model. (a) False (b) True
24. 24.True or false: “checking labels twice” is good for training a model. (a) False (b) True
25. 25.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
26. 26.True or false: “photos in many lights” is good for training a model. (a) True (b) False
27. 27.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
28. 28.Which of these is bad for training a model? (a) very few examples (b) examples of every type (c) many varied examples (d) clear, sharp examples
29. 29.True or false: “wrong labels” is good for training a model. (a) False (b) True
30. 30.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
31. 31.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
32. 32.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
33. 33.True or false: “many varied examples” is bad for training a model. (a) True (b) False
34. 34.🏷️ 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
35. 35.Which of these is good for training a model? (a) testing on training data (b) checking labels twice (c) very few examples (d) blurry, unclear photos
36. 36.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) wrong labels (b) checking labels twice (c) many varied examples (d) clear, sharp examples
38. 38.🏷️ 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
39. 39.🔍 Which is the odd one out? (a) new data for testing (b) only one kind of example (c) labels added at random (d) wrong labels
40. 40.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) testing on training data (d) examples of every type

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