Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 542 · 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 good for training a model? (a) only one kind of example (b) labels added at random (c) blurry, unclear photos (d) new data for testing
2. 2.🏷️ 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
3. 3.True or false: “photos in many lights” is good for training a model. (a) True (b) False
4. 4.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
5. 5.Which of these is good for training a model? (a) only one kind of example (b) correct labels (c) very few examples (d) testing on training data
6. 6.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
7. 7.🔍 Which is the odd one out? (a) clear, sharp examples (b) blurry, unclear photos (c) copies of one photo (d) testing on training data
8. 8.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
9. 9.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) many varied examples (d) only one kind of example
10. 10.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
11. 11.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
12. 12.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
13. 13.True or false: “new data for testing” is good for training a model. (a) True (b) False
14. 14.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
15. 15.True or false: “copies of one photo” is good for training a model. (a) True (b) False
16. 16.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) blurry, unclear photos (d) checking labels twice
17. 17.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) clear, sharp examples (d) copies of one photo
18. 18.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) wrong labels (d) checking labels twice
19. 19.True or false: “examples of every type” is bad for training a model. (a) True (b) False
20. 20.🏷️ 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
21. 21.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) wrong labels (d) many varied examples
22. 22.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
23. 23.🏷️ 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
24. 24.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) checking labels twice (d) testing on training data
25. 25.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) learn from data (d) print pages
26. 26.🔍 Which is the odd one out? (a) only one kind of example (b) blurry, unclear photos (c) very few examples (d) examples of every type
27. 27.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) photos in many lights (d) wrong labels
28. 28.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
30. 30.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
31. 31.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) examples of every type (d) testing on training data
32. 32.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
33. 33.True or false: “labels added at random” is good for training a model. (a) False (b) True
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 is the odd one out? (a) very few examples (b) examples of every type (c) photos in many lights (d) correct labels
36. 36.Which of these is bad for training a model? (a) copies of one photo (b) new data for testing (c) correct labels (d) clear, sharp examples
37. 37.Which of these is bad for training a model? (a) new data for testing (b) only one kind of example (c) checking labels twice (d) examples of every type
38. 38.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
39. 39.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
40. 40.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) blurry, unclear photos (d) examples of every type

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