Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 224 · 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 “not spam”. (a) False (b) True
2. 2.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) the font size (d) a sticker on a laptop
3. 3.🏷️ 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
4. 4.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
5. 5.Which of these is bad for training a model? (a) very few examples (b) many varied examples (c) examples of every type (d) photos in many lights
6. 6.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) photos in many lights (d) testing on training data
7. 7.🔍 Which is the odd one out? (a) very few examples (b) wrong labels (c) only one kind of example (d) photos in many lights
8. 8.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) only one kind of example (d) correct labels
9. 9.🏷️ 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
10. 10.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
11. 11.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) change colour (d) slower to switch on
12. 12.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
14. 14.🥭 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 turn yellow (d) it will be perfect
15. 15.True or false: “checking labels twice” is good for training a model. (a) False (b) True
16. 16.True or false: “new data for testing” is good for training a model. (a) False (b) True
17. 17.Which of these is good for training a model? (a) only one kind of example (b) copies of one photo (c) clear, sharp examples (d) labels added at random
18. 18.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
19. 19.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) clear, sharp examples (d) labels added at random
20. 20.True or false: “correct labels” is good for training a model. (a) False (b) True
21. 21.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) testing on training data (d) many varied examples
22. 22.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) blurry, unclear photos (d) examples of every type
23. 23.🏷️ 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
24. 24.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
25. 25.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
26. 26.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
27. 27.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
28. 28.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) only one kind of example (d) photos in many lights
29. 29.🏷️ 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
30. 30.True or false: “examples of every type” is good for training a model. (a) True (b) False
31. 31.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) photos in many lights (d) blurry, unclear photos
32. 32.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) clear, sharp examples (d) labels added at random
33. 33.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) very few examples (d) checking labels twice
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
35. 35.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
36. 36.True or false: “correct labels” is bad for training a model. (a) True (b) False
37. 37.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) clear, sharp examples (d) very few examples
39. 39.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) wrong labels (d) photos in many lights
40. 40.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True

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