Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 42 · 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.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) slower to switch on (d) change colour
2. 2.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) only one kind of example (d) clear, sharp examples
3. 3.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) new data for testing (d) labels added at random
4. 4.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it may get it wrong (d) it will turn yellow
5. 5.🔍 Which is the odd one out? (a) very few examples (b) checking labels twice (c) examples of every type (d) clear, sharp examples
6. 6.🏷️ 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
7. 7.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) many labelled photos (d) one single photo
8. 8.True or false: “photos in many lights” is good for training a model. (a) False (b) True
9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
10. 10.Which of these is good for training a model? (a) checking labels twice (b) wrong labels (c) blurry, unclear photos (d) copies of one photo
11. 11.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
12. 12.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) only one kind of example (d) wrong labels
13. 13.🔍 Which is the odd one out? (a) labels added at random (b) wrong labels (c) checking labels twice (d) very few examples
14. 14.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
15. 15.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
16. 16.🏷️ 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
17. 17.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
18. 18.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) testing on training data (d) new data for testing
20. 20.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
21. 21.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
22. 22.True or false: “wrong labels” is good for training a model. (a) True (b) False
23. 23.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) charge faster (d) learn from data
24. 24.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
25. 25.🏷️ What is a “label” in machine learning? (a) the font size (b) the right answer tag (c) a sticker on a laptop (d) the price
26. 26.Which of these is bad for training a model? (a) many varied examples (b) wrong labels (c) clear, sharp examples (d) photos in many lights
27. 27.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
28. 28.🔍 Which is the odd one out? (a) examples of every type (b) correct labels (c) checking labels twice (d) copies of one photo
29. 29.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
30. 30.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
31. 31.True or false: “very few examples” is good for training a model. (a) False (b) True
32. 32.Which of these is bad for training a model? (a) labels added at random (b) photos in many lights (c) new data for testing (d) many varied examples
33. 33.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) new data for testing (d) blurry, unclear photos
34. 34.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
35. 35.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
36. 36.🏷️ 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
37. 37.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
38. 38.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
39. 39.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) very few examples (d) examples of every type

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