Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 437 · 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.True or false: “new data for testing” is good for training a model. (a) True (b) False
2. 2.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) copies of one photo (d) photos in many lights
3. 3.Which of these is good for training a model? (a) labels added at random (b) examples of every type (c) testing on training data (d) only one kind of example
4. 4.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) wrong labels (d) testing on training data
5. 5.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
6. 6.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
7. 7.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) many labelled photos (d) a list of rules only
9. 9.Which of these is bad for training a model? (a) very few examples (b) many varied examples (c) photos in many lights (d) correct labels
10. 10.True or false: “correct labels” is good for training a model. (a) True (b) False
11. 11.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) correct labels (d) clear, sharp examples
12. 12.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
13. 13.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
14. 14.Which of these is good for training a model? (a) testing on training data (b) new data for testing (c) blurry, unclear photos (d) only one kind of example
15. 15.🏷️ 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
16. 16.True or false: “testing on training data” is bad for training a model. (a) False (b) True
17. 17.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) testing on training data (d) blurry, unclear photos
18. 18.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
19. 19.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
20. 20.Which of these is good for training a model? (a) clear, sharp examples (b) copies of one photo (c) wrong labels (d) very few examples
21. 21.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
22. 22.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
23. 23.True or false: “many varied examples” is good for training a model. (a) False (b) True
24. 24.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) copies of one photo (d) checking labels twice
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
26. 26.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) many varied examples (d) blurry, unclear photos
27. 27.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
28. 28.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
30. 30.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
31. 31.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
32. 32.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will turn yellow (d) it will shut down
33. 33.🔍 Which is the odd one out? (a) examples of every type (b) blurry, unclear photos (c) photos in many lights (d) clear, sharp examples
34. 34.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) copies of one photo (d) checking labels twice
35. 35.True or false: “wrong labels” is good for training a model. (a) True (b) False
36. 36.True or false: “very few examples” is bad for training a model. (a) False (b) True
37. 37.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) slower to switch on (d) better at its task
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
39. 39.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) blurry, unclear photos (d) clear, sharp examples
40. 40.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training

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