Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 165 · 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.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) labels added at random (d) clear, sharp examples
2. 2.🔍 Which is the odd one out? (a) checking labels twice (b) photos in many lights (c) wrong labels (d) clear, sharp examples
3. 3.🏷️ 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
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) a song about dogs (d) one single photo
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
6. 6.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) very few examples (d) new data for testing
7. 7.Which of these is good for training a model? (a) only one kind of example (b) checking labels twice (c) labels added at random (d) wrong labels
8. 8.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
9. 9.🔍 Which is the odd one out? (a) photos in many lights (b) wrong labels (c) only one kind of example (d) testing on training data
10. 10.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) correct labels (d) testing on training data
11. 11.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
12. 12.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
14. 14.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
15. 15.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
16. 16.🔍 Which is the odd one out? (a) clear, sharp examples (b) blurry, unclear photos (c) checking labels twice (d) correct labels
17. 17.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) clear, sharp examples (d) only one kind of example
18. 18.🔍 Which is the odd one out? (a) blurry, unclear photos (b) checking labels twice (c) testing on training data (d) copies of one photo
19. 19.True or false: “photos in many lights” is good for training a model. (a) True (b) False
20. 20.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
21. 21.True or false: “wrong labels” is good for training a model. (a) True (b) False
22. 22.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
23. 23.Which of these is good for training a model? (a) labels added at random (b) photos in many lights (c) very few examples (d) copies of one photo
24. 24.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) clear, sharp examples (d) copies of one photo
25. 25.Which of these is good for training a model? (a) correct labels (b) copies of one photo (c) testing on training data (d) only one kind of example
26. 26.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) clear, sharp examples (d) labels added at random
27. 27.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
28. 28.🏷️ 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
29. 29.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
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 is the odd one out? (a) testing on training data (b) copies of one photo (c) labels added at random (d) examples of every type
32. 32.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on
33. 33.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
34. 34.True or false: “wrong labels” is bad for training a model. (a) False (b) True
35. 35.🏷️ 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
36. 36.True or false: “labels added at random” is good for training a model. (a) False (b) True
37. 37.True or false: “examples of every type” is good for training a model. (a) True (b) False
38. 38.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
39. 39.True or false: “copies of one photo” is good for training a model. (a) False (b) True
40. 40.True or false: “many varied examples” is good for training a model. (a) False (b) True

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