Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 419 · 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) many varied examples (b) testing on training data (c) clear, sharp examples (d) photos in many lights
2. 2.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
3. 3.True or false: “new data for testing” is good for training a model. (a) False (b) True
4. 4.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
5. 5.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) wrong labels (d) correct labels
6. 6.True or false: “testing on training data” is good for training a model. (a) True (b) False
7. 7.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
8. 8.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
9. 9.Which of these is good for training a model? (a) only one kind of example (b) new data for testing (c) labels added at random (d) wrong labels
10. 10.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
11. 11.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
12. 12.Which of these is bad for training a model? (a) examples of every type (b) copies of one photo (c) clear, sharp examples (d) checking labels twice
13. 13.📚 Good or bad for training a model: clear, sharp examples? (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) spam (b) not spam
15. 15.🔍 Which is the odd one out? (a) correct labels (b) examples of every type (c) many varied examples (d) very few examples
16. 16.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
17. 17.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
18. 18.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
19. 19.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
20. 20.🔍 Which is the odd one out? (a) clear, sharp examples (b) many varied examples (c) copies of one photo (d) new data for testing
21. 21.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
22. 22.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
23. 23.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
24. 24.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
25. 25.True or false: “labels added at random” is good for training a model. (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) new data for testing (b) testing on training data (c) very few examples (d) only one kind of example
27. 27.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) copies of one photo (d) wrong labels
28. 28.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) one single photo (d) a song about dogs
29. 29.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will turn yellow (d) it will shut down
30. 30.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) labels added at random (d) correct labels
31. 31.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
32. 32.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) testing on training data (d) photos in many lights
33. 33.Which of these is good for training a model? (a) many varied examples (b) copies of one photo (c) wrong labels (d) testing on training data
34. 34.Which of these is bad for training a model? (a) labels added at random (b) correct labels (c) new data for testing (d) clear, sharp examples
35. 35.True or false: “many varied examples” is bad for training a model. (a) False (b) True
36. 36.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
37. 37.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) clear, sharp examples (d) very few examples
38. 38.🏷️ 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
39. 39.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
40. 40.Which of these is bad for training a model? (a) wrong labels (b) examples of every type (c) many varied examples (d) checking labels twice

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