Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 238 · 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 good for training a model? (a) only one kind of example (b) new data for testing (c) very few examples (d) wrong labels
2. 2.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
3. 3.🏷️ 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
4. 4.🏷️ 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
5. 5.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
6. 6.🏷️ 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
7. 7.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) copies of one photo (d) checking labels twice
8. 8.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the price (d) the font size
9. 9.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
10. 10.True or false: “only one kind of example” is good for training a model. (a) False (b) True
11. 11.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
12. 12.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
13. 13.True or false: “examples of every type” is good for training a model. (a) True (b) False
14. 14.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) labels added at random (d) many varied examples
15. 15.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
16. 16.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) blurry, unclear photos (d) clear, sharp examples
17. 17.True or false: “correct labels” is bad for training a model. (a) False (b) True
18. 18.Which of these is bad for training a model? (a) examples of every type (b) photos in many lights (c) very few examples (d) clear, sharp examples
19. 19.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
21. 21.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
22. 22.🏷️ 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
23. 23.Which of these is good for training a model? (a) blurry, unclear photos (b) only one kind of example (c) photos in many lights (d) very few examples
24. 24.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
25. 25.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) only one kind of example (d) blurry, unclear photos
27. 27.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
28. 28.True or false: “labels added at random” is bad for training a model. (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) checking labels twice (b) clear, sharp examples (c) wrong labels (d) correct labels
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will shut down (c) it will be perfect (d) it will turn yellow
31. 31.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
32. 32.🔍 Which is the odd one out? (a) testing on training data (b) new data for testing (c) labels added at random (d) very few examples
33. 33.🔍 Which is the odd one out? (a) checking labels twice (b) blurry, unclear photos (c) examples of every type (d) new data for testing
34. 34.Which of these is good for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) very few examples (d) copies of one photo
35. 35.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
36. 36.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
37. 37.True or false: “examples of every type” is bad for training a model. (a) False (b) True
38. 38.🔍 Which is the odd one out? (a) photos in many lights (b) labels added at random (c) examples of every type (d) new data for testing
39. 39.True or false: “very few examples” is bad for training a model. (a) False (b) True
40. 40.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on

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