Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 232 · 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: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
2. 2.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
3. 3.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) very few examples (d) examples of every type
4. 4.Which of these is good for training a model? (a) many varied examples (b) labels added at random (c) very few examples (d) blurry, unclear photos
5. 5.🏷️ 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
6. 6.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) clear, sharp examples (d) checking labels twice
7. 7.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
8. 8.🔍 Which is the odd one out? (a) only one kind of example (b) examples of every type (c) many varied examples (d) correct labels
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) many labelled photos (d) one single photo
10. 10.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
11. 11.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
13. 13.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
14. 14.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
15. 15.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
16. 16.🏷️ 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
17. 17.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) new data for testing (d) very few examples
18. 18.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
19. 19.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
21. 21.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) wrong labels (d) checking labels twice
22. 22.🔍 Which is the odd one out? (a) wrong labels (b) examples of every type (c) copies of one photo (d) testing on training data
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
24. 24.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
25. 25.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
26. 26.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
27. 27.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
28. 28.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) many varied examples (d) testing on training data
29. 29.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
30. 30.🏷️ 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
31. 31.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
32. 32.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
33. 33.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) wrong labels (d) new data for testing
34. 34.True or false: “copies of one photo” is good for training a model. (a) False (b) True
35. 35.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) correct labels (d) very few examples
36. 36.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) new data for testing (d) clear, sharp examples
37. 37.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) copies of one photo (d) new data for testing
38. 38.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) the right answer tag (d) a sticker on a laptop
39. 39.Which of these is bad for training a model? (a) wrong labels (b) clear, sharp examples (c) many varied examples (d) new data for testing
40. 40.Which of these is bad for training a model? (a) new data for testing (b) checking labels twice (c) very few examples (d) examples of every type

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