Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 162 · 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.🏷️ 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
2. 2.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) wrong labels (d) clear, sharp examples
3. 3.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
4. 4.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
5. 5.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) testing on training data (d) correct labels
6. 6.🏷️ What is a “label” in machine learning? (a) the font size (b) a sticker on a laptop (c) the right answer tag (d) the price
7. 7.True or false: “many varied examples” is bad for training a model. (a) False (b) True
8. 8.Which of these is good for training a model? (a) blurry, unclear photos (b) checking labels twice (c) testing on training data (d) labels added at random
9. 9.True or false: “photos in many lights” is good for training a model. (a) False (b) True
10. 10.🏷️ 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
11. 11.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
12. 12.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) only one kind of example (d) testing on training data
13. 13.🔍 Which is the odd one out? (a) correct labels (b) testing on training data (c) wrong labels (d) very few examples
14. 14.True or false: “correct labels” is bad for training a model. (a) False (b) True
15. 15.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
16. 16.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
17. 17.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) one single photo (d) a song about dogs
18. 18.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it may get it wrong (d) it will be perfect
19. 19.True or false: “labels added at random” is good for training a model. (a) False (b) True
20. 20.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
21. 21.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) only one kind of example (d) clear, sharp examples
22. 22.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) learn from data (d) clean screens
23. 23.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
24. 24.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
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.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
27. 27.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
28. 28.True or false: “wrong labels” is good for training a model. (a) True (b) False
29. 29.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
30. 30.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) very few examples (d) correct labels
31. 31.Which of these is good for training a model? (a) blurry, unclear photos (b) correct labels (c) only one kind of example (d) copies of one photo
32. 32.🔍 Which is the odd one out? (a) testing on training data (b) labels added at random (c) photos in many lights (d) copies of one photo
33. 33.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
34. 34.True or false: “new data for testing” is bad for training a model. (a) False (b) True
35. 35.🔍 Which is the odd one out? (a) checking labels twice (b) very few examples (c) photos in many lights (d) examples of every type
36. 36.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
37. 37.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
38. 38.Which of these is bad for training a model? (a) checking labels twice (b) copies of one photo (c) examples of every type (d) photos in many lights
39. 39.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
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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