Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 341 · 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.🥭 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 may get it wrong (d) it will turn yellow
2. 2.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
3. 3.Which of these is bad for training a model? (a) new data for testing (b) labels added at random (c) many varied examples (d) photos in many lights
4. 4.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
5. 5.🏷️ 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
6. 6.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
7. 7.True or false: “examples of every type” is bad for training a model. (a) False (b) True
8. 8.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
9. 9.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
10. 10.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
11. 11.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) very few examples (d) wrong labels
12. 12.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
13. 13.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) checking labels twice (d) copies of one photo
14. 14.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) copies of one photo (d) clear, sharp examples
15. 15.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
16. 16.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
17. 17.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) slower to switch on (d) forget everything
18. 18.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
19. 19.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the price (d) the font size
20. 20.True or false: “many varied examples” is bad for training a model. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) clear, sharp examples (d) correct labels
22. 22.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) testing on training data (d) correct labels
23. 23.🏷️ 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
24. 24.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
25. 25.Which of these is bad for training a model? (a) blurry, unclear photos (b) correct labels (c) examples of every type (d) photos in many lights
26. 26.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
27. 27.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
28. 28.🔍 Which is the odd one out? (a) very few examples (b) wrong labels (c) many varied examples (d) only one kind of example
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) spam (b) not spam
30. 30.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
31. 31.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) clear, sharp examples (d) wrong labels
32. 32.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
33. 33.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) blurry, unclear photos (d) new data for testing
34. 34.Which of these is good for training a model? (a) very few examples (b) examples of every type (c) only one kind of example (d) testing on training data
35. 35.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
36. 36.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
37. 37.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
38. 38.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
39. 39.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
40. 40.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) wrong labels (d) new data for testing

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