Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 142 · 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.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
2. 2.True or false: “wrong labels” is good for training a model. (a) True (b) False
3. 3.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
4. 4.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
5. 5.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
7. 7.True or false: “correct labels” is bad for training a model. (a) False (b) True
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
9. 9.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
10. 10.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
11. 11.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) labels added at random (d) testing on training data
12. 12.🏷️ 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
13. 13.🔍 Which is the odd one out? (a) clear, sharp examples (b) blurry, unclear photos (c) many varied examples (d) checking labels twice
14. 14.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
15. 15.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
16. 16.Which of these is good for training a model? (a) testing on training data (b) examples of every type (c) only one kind of example (d) copies of one photo
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.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
19. 19.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
20. 20.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) blurry, unclear photos (d) correct labels
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: “checking labels twice” is good for training a model. (a) True (b) False
23. 23.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) copies of one photo (d) new data for testing
24. 24.🏷️ 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
25. 25.Which of these is bad for training a model? (a) testing on training data (b) new data for testing (c) examples of every type (d) many varied examples
26. 26.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
27. 27.True or false: “many varied examples” is bad for training a model. (a) True (b) False
28. 28.True or false: “wrong labels” is bad for training a model. (a) False (b) True
29. 29.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) wrong labels (d) checking labels twice
30. 30.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
31. 31.🔍 Which is the odd one out? (a) only one kind of example (b) examples of every type (c) very few examples (d) blurry, unclear photos
32. 32.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) blurry, unclear photos (d) many varied examples
33. 33.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) only one kind of example (d) correct labels
34. 34.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
35. 35.🔍 Which is the odd one out? (a) blurry, unclear photos (b) copies of one photo (c) testing on training data (d) many varied examples
36. 36.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
37. 37.True or false: “photos in many lights” is good for training a model. (a) False (b) True
38. 38.Which of these is bad for training a model? (a) new data for testing (b) copies of one photo (c) examples of every type (d) correct labels
39. 39.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) very few examples (d) photos in many lights
40. 40.True or false: “testing on training data” is bad for training a model. (a) True (b) False

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