Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 130 · 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: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
2. 2.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) only one kind of example (d) wrong labels
3. 3.Which of these is good for training a model? (a) clear, sharp examples (b) wrong labels (c) copies of one photo (d) blurry, unclear photos
4. 4.🏷️ 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
5. 5.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
6. 6.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
7. 7.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
8. 8.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) wrong labels (d) photos in many lights
9. 9.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
10. 10.🔍 Which is the odd one out? (a) clear, sharp examples (b) wrong labels (c) examples of every type (d) photos in many lights
11. 11.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) wrong labels (d) checking labels twice
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
13. 13.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) correct labels (d) blurry, unclear photos
14. 14.True or false: “examples of every type” is bad for training a model. (a) True (b) False
15. 15.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
16. 16.True or false: “new data for testing” is good for training a model. (a) False (b) True
17. 17.🏷️ 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
18. 18.🏷️ 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
19. 19.🏷️ 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
20. 20.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
21. 21.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
22. 22.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
23. 23.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
24. 24.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) checking labels twice (d) wrong labels
25. 25.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) testing on training data (d) correct labels
26. 26.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
27. 27.🏷️ 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
28. 28.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) correct labels (d) very few examples
29. 29.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it may get it wrong (d) it will be perfect
31. 31.🔍 Which is the odd one out? (a) blurry, unclear photos (b) correct labels (c) checking labels twice (d) new data for testing
32. 32.True or false: “checking labels twice” is good for training a model. (a) False (b) True
33. 33.True or false: “testing on training data” is bad for training a model. (a) False (b) True
34. 34.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
35. 35.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
36. 36.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
37. 37.True or false: “many varied examples” is bad for training a model. (a) False (b) True
38. 38.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
39. 39.🏷️ 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
40. 40.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training

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