Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 318 · 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: testing on training data? (a) good for training (b) bad for training
2. 2.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
3. 3.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
4. 4.🏷️ 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
5. 5.True or false: “correct labels” is bad for training a model. (a) False (b) True
6. 6.📚 Usually, more good examples make a model… (a) slower to switch on (b) better at its task (c) forget everything (d) change colour
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) not spam (b) spam
8. 8.Which of these is bad for training a model? (a) new data for testing (b) checking labels twice (c) only one kind of example (d) examples of every type
9. 9.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) one single photo (d) a song about dogs
11. 11.🏷️ 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
12. 12.Which of these is good for training a model? (a) testing on training data (b) clear, sharp examples (c) wrong labels (d) labels added at random
13. 13.🔍 Which is the odd one out? (a) wrong labels (b) labels added at random (c) only one kind of example (d) photos in many lights
14. 14.Which of these is good for training a model? (a) checking labels twice (b) testing on training data (c) wrong labels (d) very few examples
15. 15.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
16. 16.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
17. 17.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
18. 18.🔍 Which is the odd one out? (a) labels added at random (b) many varied examples (c) blurry, unclear photos (d) wrong labels
19. 19.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) photos in many lights (d) blurry, unclear photos
20. 20.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
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: “copies of one photo” is bad for training a model. (a) False (b) True
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: “clear, sharp examples” is bad for training a model. (a) False (b) True
25. 25.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
26. 26.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
27. 27.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) a sticker on a laptop (d) the font size
28. 28.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
29. 29.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) blurry, unclear photos (d) checking labels twice
30. 30.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) new data for testing (d) very few examples
31. 31.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) only one kind of example (d) new data for testing
32. 32.True or false: “testing on training data” is bad for training a model. (a) False (b) True
33. 33.True or false: “checking labels twice” is good for training a model. (a) True (b) False
34. 34.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) correct labels (d) testing on training data
35. 35.True or false: “labels added at random” is bad for training a model. (a) True (b) False
36. 36.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) checking labels twice (d) testing on training data
37. 37.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
38. 38.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
39. 39.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
40. 40.🏷️ 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

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