Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 322 · 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: “correct labels” is good for training a model. (a) False (b) True
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
3. 3.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) a list of rules only (d) one single photo
5. 5.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) checking labels twice (d) testing on training data
6. 6.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the font size (d) the price
7. 7.🏷️ 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
8. 8.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) testing on training data (d) many varied examples
9. 9.True or false: “testing on training data” is good for training a model. (a) True (b) False
10. 10.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
11. 11.True or false: “wrong labels” is bad for training a model. (a) False (b) True
12. 12.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
13. 13.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
14. 14.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) slower to switch on (d) better at its task
15. 15.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) labels added at random (d) new data for testing
16. 16.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
17. 17.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
18. 18.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
19. 19.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) many varied examples (d) blurry, unclear photos
20. 20.Which of these is good for training a model? (a) blurry, unclear photos (b) correct labels (c) wrong labels (d) very few examples
21. 21.🏷️ 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
22. 22.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
23. 23.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
24. 24.True or false: “photos in many lights” is good for training a model. (a) True (b) False
25. 25.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) labels added at random (d) clear, sharp examples
26. 26.🔍 Which is the odd one out? (a) copies of one photo (b) clear, sharp examples (c) correct labels (d) checking labels twice
27. 27.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
28. 28.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) many varied examples (d) copies of one photo
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
31. 31.Which of these is bad for training a model? (a) many varied examples (b) very few examples (c) clear, sharp examples (d) new data for testing
32. 32.🏷️ 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
33. 33.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) wrong labels (d) photos in many lights
34. 34.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
35. 35.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) testing on training data (d) correct labels
36. 36.🏷️ 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
37. 37.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will shut down (d) it will be perfect
38. 38.Which of these is bad for training a model? (a) photos in many lights (b) wrong labels (c) correct labels (d) new data for testing
39. 39.🔍 Which is the odd one out? (a) checking labels twice (b) labels added at random (c) examples of every type (d) many varied examples
40. 40.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) clear, sharp examples (d) wrong labels

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