Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 233 · 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.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
2. 2.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
3. 3.True or false: “new data for testing” is bad for training a model. (a) True (b) False
4. 4.🏷️ 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
5. 5.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on
6. 6.Which of these is good for training a model? (a) labels added at random (b) new data for testing (c) only one kind of example (d) very few examples
7. 7.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
8. 8.True or false: “copies of one photo” is good for training a model. (a) False (b) True
9. 9.🏷️ 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
10. 10.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
11. 11.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
12. 12.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
13. 13.True or false: “wrong labels” is good for training a model. (a) False (b) True
14. 14.True or false: “examples of every type” is bad for training a model. (a) True (b) False
15. 15.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
16. 16.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) new data for testing (d) only one kind of example
17. 17.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
18. 18.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) correct labels (d) very few examples
19. 19.Which of these is bad for training a model? (a) wrong labels (b) clear, sharp examples (c) photos in many lights (d) many varied examples
20. 20.🏷️ 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
21. 21.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
22. 22.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) copies of one photo (d) correct labels
23. 23.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
24. 24.Which of these is bad for training a model? (a) checking labels twice (b) only one kind of example (c) correct labels (d) new data for testing
25. 25.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
26. 26.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) examples of every type (b) correct labels (c) very few examples (d) many varied examples
28. 28.🔍 Which is the odd one out? (a) new data for testing (b) testing on training data (c) checking labels twice (d) photos in many lights
29. 29.🔍 Which is the odd one out? (a) blurry, unclear photos (b) examples of every type (c) checking labels twice (d) correct labels
30. 30.🏷️ 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
31. 31.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
32. 32.True or false: “many varied examples” is good for training a model. (a) True (b) False
33. 33.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) labels added at random (d) new data for testing
34. 34.Which of these is good for training a model? (a) labels added at random (b) checking labels twice (c) testing on training data (d) very few examples
35. 35.True or false: “new data for testing” is good for training a model. (a) False (b) True
36. 36.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (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: many varied examples? (a) bad for training (b) good for training
39. 39.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
40. 40.True or false: “correct labels” is good for training a model. (a) True (b) False

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