Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 54 · 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.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) clean screens (d) charge faster
2. 2.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
3. 3.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) clear, sharp examples (d) very few examples
4. 4.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
5. 5.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
6. 6.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
7. 7.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) new data for testing (d) blurry, unclear photos
8. 8.True or false: “examples of every type” is bad for training a model. (a) False (b) True
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) many labelled photos (d) one single photo
10. 10.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
11. 11.True or false: “labels added at random” is bad for training a model. (a) False (b) True
12. 12.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
14. 14.🏷️ 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
15. 15.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
16. 16.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
17. 17.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) checking labels twice (d) examples of every type
18. 18.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
19. 19.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
20. 20.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
21. 21.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
22. 22.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) slower to switch on (d) forget everything
23. 23.🏷️ 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
24. 24.True or false: “wrong labels” is bad for training a model. (a) True (b) False
25. 25.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
26. 26.Which of these is bad for training a model? (a) testing on training data (b) examples of every type (c) clear, sharp examples (d) correct labels
27. 27.🏷️ 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
28. 28.🏷️ 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
29. 29.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
30. 30.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) copies of one photo (d) examples of every type
31. 31.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) labels added at random (d) many varied examples
32. 32.True or false: “correct labels” is bad for training a model. (a) False (b) True
33. 33.🏷️ 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
34. 34.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
35. 35.🔍 Which is the odd one out? (a) new data for testing (b) blurry, unclear photos (c) many varied examples (d) checking labels twice
36. 36.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
37. 37.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
38. 38.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
39. 39.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
40. 40.Which of these is bad for training a model? (a) new data for testing (b) very few examples (c) photos in many lights (d) many varied examples

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