Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 303 · 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 “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
2. 2.Which of these is good for training a model? (a) clear, sharp examples (b) very few examples (c) testing on training data (d) only one kind of example
3. 3.Which of these is good for training a model? (a) correct labels (b) only one kind of example (c) wrong labels (d) copies of one photo
4. 4.True or false: “correct labels” is good for training a model. (a) True (b) False
5. 5.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) very few examples (b) wrong labels (c) many varied examples (d) only one kind of example
7. 7.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
8. 8.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) testing on training data (d) new data for testing
9. 9.🏷️ 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
10. 10.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
11. 11.True or false: “wrong labels” is bad for training a model. (a) True (b) False
12. 12.Which of these is bad for training a model? (a) copies of one photo (b) new data for testing (c) checking labels twice (d) many varied examples
13. 13.🏷️ 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
14. 14.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) clean screens (d) charge faster
15. 15.🏷️ 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
16. 16.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) correct labels (d) very few examples
17. 17.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) blurry, unclear photos (d) wrong labels
18. 18.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it will turn yellow (d) it may get it wrong
19. 19.True or false: “testing on training data” is bad for training a model. (a) False (b) True
20. 20.True or false: “only one kind of example” is good for training a model. (a) False (b) True
21. 21.True or false: “checking labels twice” is good for training a model. (a) False (b) True
22. 22.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
23. 23.True or false: “wrong labels” is good for training a model. (a) True (b) False
24. 24.Which of these is good for training a model? (a) examples of every type (b) wrong labels (c) very few examples (d) testing on training data
25. 25.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
26. 26.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) examples of every type (d) blurry, unclear photos
27. 27.🏷️ 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
28. 28.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
29. 29.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
30. 30.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
31. 31.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) clear, sharp examples (d) very few examples
32. 32.🏷️ 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
33. 33.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) examples of every type (d) wrong labels
34. 34.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
35. 35.Which of these is good for training a model? (a) blurry, unclear photos (b) checking labels twice (c) testing on training data (d) very few examples
36. 36.🏷️ 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
37. 37.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
39. 39.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
40. 40.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) many varied examples (d) labels added at random

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