Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 134 · 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: only one kind of example? (a) bad for training (b) good for training
2. 2.🏷️ 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
3. 3.Which of these is good for training a model? (a) new data for testing (b) very few examples (c) copies of one photo (d) wrong labels
4. 4.Which of these is bad for training a model? (a) examples of every type (b) labels added at random (c) correct labels (d) checking labels twice
5. 5.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) labels added at random (d) examples of every type
7. 7.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will be perfect (d) it will turn yellow
8. 8.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) only one kind of example (d) new data for testing
9. 9.True or false: “wrong labels” is good for training a model. (a) True (b) False
10. 10.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
11. 11.🔍 Which is the odd one out? (a) checking labels twice (b) new data for testing (c) very few examples (d) clear, sharp examples
12. 12.🔍 Which is the odd one out? (a) photos in many lights (b) only one kind of example (c) clear, sharp examples (d) checking labels twice
13. 13.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) correct labels (d) very few examples
14. 14.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
15. 15.Which of these is bad for training a model? (a) photos in many lights (b) copies of one photo (c) examples of every type (d) new data for testing
16. 16.🏷️ 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
17. 17.🏷️ 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
18. 18.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a list of rules only (d) a song about dogs
19. 19.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) a sticker on a laptop (d) the price
20. 20.True or false: “testing on training data” is good for training a model. (a) False (b) True
21. 21.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
22. 22.🔍 Which is the odd one out? (a) correct labels (b) copies of one photo (c) very few examples (d) wrong labels
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: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
25. 25.True or false: “new data for testing” is good for training a model. (a) True (b) False
26. 26.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
27. 27.Which of these is good for training a model? (a) clear, sharp examples (b) copies of one photo (c) blurry, unclear photos (d) labels added at random
28. 28.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
29. 29.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
30. 30.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
31. 31.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) copies of one photo (d) many varied examples
32. 32.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
33. 33.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
34. 34.Which of these is bad for training a model? (a) new data for testing (b) clear, sharp examples (c) very few examples (d) many varied examples
35. 35.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
36. 36.🏷️ 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
37. 37.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
38. 38.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
39. 39.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
40. 40.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False

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