Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 317 · 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.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) many labelled photos (c) one single photo (d) a list of rules only
2. 2.Which of these is bad for training a model? (a) checking labels twice (b) wrong labels (c) photos in many lights (d) clear, sharp examples
3. 3.True or false: “many varied examples” is good for training a model. (a) True (b) False
4. 4.🏷️ 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
5. 5.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
6. 6.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
7. 7.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
8. 8.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
9. 9.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
10. 10.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
11. 11.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
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) not spam (b) spam
13. 13.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
14. 14.🏷️ 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
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
16. 16.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
17. 17.Which of these is bad for training a model? (a) new data for testing (b) very few examples (c) checking labels twice (d) many varied examples
18. 18.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
19. 19.🏷️ 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
20. 20.Which of these is good for training a model? (a) only one kind of example (b) labels added at random (c) photos in many lights (d) testing on training data
21. 21.True or false: “many varied examples” is bad for training a model. (a) False (b) True
22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
23. 23.True or false: “very few examples” is good for training a model. (a) True (b) False
24. 24.True or false: “labels added at random” is good for training a model. (a) True (b) False
25. 25.True or false: “photos in many lights” is good for training a model. (a) False (b) True
26. 26.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
27. 27.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) copies of one photo (d) clear, sharp examples
28. 28.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
29. 29.Which of these is bad for training a model? (a) blurry, unclear photos (b) correct labels (c) many varied examples (d) checking labels twice
30. 30.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
31. 31.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
32. 32.🏷️ 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
33. 33.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
34. 34.🔍 Which is the odd one out? (a) testing on training data (b) new data for testing (c) examples of every type (d) clear, sharp examples
35. 35.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
36. 36.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
37. 37.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it will be perfect (d) it may get it wrong
38. 38.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
39. 39.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) examples of every type (d) blurry, unclear photos
40. 40.🏷️ 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

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