Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 336 · 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 of these is bad for training a model? (a) new data for testing (b) copies of one photo (c) correct labels (d) photos in many lights
2. 2.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
3. 3.Which of these is good for training a model? (a) only one kind of example (b) many varied examples (c) wrong labels (d) blurry, unclear photos
4. 4.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
5. 5.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
6. 6.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) labels added at random (d) correct labels
8. 8.🏷️ 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
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
10. 10.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
11. 11.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
12. 12.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) clear, sharp examples (d) testing on training data
13. 13.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
14. 14.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) photos in many lights (d) copies of one photo
15. 15.🏷️ 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
16. 16.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) testing on training data (d) photos in many lights
17. 17.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
18. 18.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
19. 19.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
20. 20.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
21. 21.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
22. 22.True or false: “new data for testing” is bad for training a model. (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) labels added at random (d) clear, sharp examples
24. 24.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) photos in many lights (d) blurry, unclear photos
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
26. 26.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will be perfect (c) it will shut down (d) it may get it wrong
27. 27.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
28. 28.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) new data for testing (d) very few examples
30. 30.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
31. 31.True or false: “correct labels” is good for training a model. (a) False (b) True
32. 32.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
33. 33.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) testing on training data (d) new data for testing
34. 34.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
35. 35.True or false: “very few examples” is bad for training a model. (a) True (b) False
36. 36.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
37. 37.🏷️ 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
38. 38.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) new data for testing (d) testing on training data
39. 39.True or false: “many varied examples” is good for training a model. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) labels added at random (d) blurry, unclear photos

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