Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 399 · 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.🏷️ 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
2. 2.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
3. 3.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will be perfect (c) it will shut down (d) it may get it wrong
4. 4.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
5. 5.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
6. 6.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) one single photo (d) a song about dogs
7. 7.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
8. 8.Which of these is bad for training a model? (a) examples of every type (b) checking labels twice (c) correct labels (d) copies of one photo
9. 9.🔍 Which is the odd one out? (a) wrong labels (b) labels added at random (c) copies of one photo (d) photos in many lights
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
11. 11.🏷️ 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
12. 12.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) only one kind of example (d) checking labels twice
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
14. 14.🏷️ 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
15. 15.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) very few examples (d) many varied examples
16. 16.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
17. 17.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
18. 18.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
20. 20.True or false: “correct labels” is bad for training a model. (a) False (b) True
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: “many varied examples” is bad for training a model. (a) True (b) False
23. 23.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) examples of every type (d) copies of one photo
24. 24.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
25. 25.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
26. 26.True or false: “testing on training data” is good for training a model. (a) True (b) False
27. 27.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
28. 28.True or false: “very few examples” is bad for training a model. (a) False (b) True
29. 29.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
30. 30.True or false: “examples of every type” is good for training a model. (a) False (b) True
31. 31.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
32. 32.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
33. 33.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
34. 34.True or false: “labels added at random” is bad for training a model. (a) True (b) False
35. 35.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
36. 36.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
37. 37.🔍 Which is the odd one out? (a) many varied examples (b) wrong labels (c) only one kind of example (d) very few examples
38. 38.🔍 Which is the odd one out? (a) labels added at random (b) checking labels twice (c) copies of one photo (d) testing on training data
39. 39.🔍 Which is the odd one out? (a) many varied examples (b) only one kind of example (c) checking labels twice (d) clear, sharp examples
40. 40.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False

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