Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 534 · 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 of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) labels added at random (d) correct labels
2. 2.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
3. 3.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
4. 4.True or false: “new data for testing” is good for training a model. (a) True (b) False
5. 5.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
6. 6.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
7. 7.Which of these is good for training a model? (a) wrong labels (b) correct labels (c) very few examples (d) only one kind of example
8. 8.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
10. 10.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
11. 11.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) copies of one photo (d) examples of every type
12. 12.True or false: “testing on training data” is bad for training a model. (a) True (b) False
13. 13.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) blurry, unclear photos (d) many varied examples
14. 14.Which of these is good for training a model? (a) only one kind of example (b) copies of one photo (c) very few examples (d) clear, sharp examples
15. 15.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
16. 16.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) copies of one photo (d) photos in many lights
17. 17.🏷️ 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
18. 18.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
19. 19.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
20. 20.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) photos in many lights (d) labels added at random
21. 21.🏷️ 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
22. 22.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
23. 23.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
24. 24.True or false: “many varied examples” is good for training a model. (a) True (b) False
25. 25.Which of these is good for training a model? (a) checking labels twice (b) copies of one photo (c) testing on training data (d) blurry, unclear photos
26. 26.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
27. 27.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
28. 28.True or false: “testing on training data” is good for training a model. (a) True (b) False
29. 29.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) labels added at random (d) testing on training data
30. 30.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) very few examples (d) many varied examples
31. 31.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
32. 32.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
33. 33.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) one single photo (d) a song about dogs
34. 34.Which of these is good for training a model? (a) only one kind of example (b) new data for testing (c) testing on training data (d) very few examples
35. 35.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it will turn yellow (d) it may get it wrong
36. 36.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
37. 37.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) charge faster (d) clean screens
38. 38.🔍 Which is the odd one out? (a) wrong labels (b) examples of every type (c) correct labels (d) photos in many lights
39. 39.Which of these is bad for training a model? (a) checking labels twice (b) copies of one photo (c) correct labels (d) many varied examples
40. 40.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training

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