Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 423 · 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? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
2. 2.🏷️ 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
3. 3.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) clear, sharp examples (d) blurry, unclear photos
4. 4.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) the right answer tag (d) a sticker on a laptop
5. 5.🏷️ 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
6. 6.Which of these is bad for training a model? (a) only one kind of example (b) many varied examples (c) correct labels (d) photos in many lights
7. 7.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
8. 8.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
9. 9.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) only one kind of example (d) very few examples
10. 10.🏷️ 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
11. 11.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
12. 12.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
13. 13.🔍 Which is the odd one out? (a) photos in many lights (b) copies of one photo (c) checking labels twice (d) new data for testing
14. 14.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
15. 15.True or false: “testing on training data” is bad for training a model. (a) False (b) True
16. 16.🏷️ 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
17. 17.🔍 Which is the odd one out? (a) copies of one photo (b) labels added at random (c) checking labels twice (d) blurry, unclear photos
18. 18.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) blurry, unclear photos (d) clear, sharp examples
19. 19.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
20. 20.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
21. 21.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) examples of every type (d) very few examples
22. 22.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) many varied examples (d) only one kind of example
23. 23.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
24. 24.🥭 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
25. 25.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
26. 26.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
27. 27.Which of these is good for training a model? (a) blurry, unclear photos (b) checking labels twice (c) only one kind of example (d) labels added at random
28. 28.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) new data for testing (d) many varied examples
29. 29.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
30. 30.True or false: “copies of one photo” is good for training a model. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) only one kind of example (d) clear, sharp examples
32. 32.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) checking labels twice (d) testing on training data
33. 33.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
34. 34.🏷️ 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
35. 35.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
36. 36.🔍 Which is the odd one out? (a) copies of one photo (b) photos in many lights (c) wrong labels (d) very few examples
37. 37.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
38. 38.Which of these is bad for training a model? (a) blurry, unclear photos (b) correct labels (c) photos in many lights (d) examples of every type
39. 39.🏷️ 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
40. 40.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) only one kind of example (d) blurry, unclear photos

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