Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 435 · 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.True or false: “new data for testing” is good for training a model. (a) False (b) True
2. 2.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) very few examples (d) photos in many lights
3. 3.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
4. 4.Which of these is good for training a model? (a) only one kind of example (b) many varied examples (c) blurry, unclear photos (d) testing on training data
5. 5.🔍 Which is the odd one out? (a) checking labels twice (b) blurry, unclear photos (c) correct labels (d) clear, sharp examples
6. 6.🏷️ 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
7. 7.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) one single photo (d) a list of rules only
9. 9.🏷️ 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
10. 10.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
11. 11.True or false: “examples of every type” is good for training a model. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) testing on training data (d) correct labels
13. 13.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
14. 14.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
16. 16.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
17. 17.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
18. 18.🏷️ 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
19. 19.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) examples of every type (d) copies of one photo
20. 20.True or false: “examples of every type” is bad for training a model. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) only one kind of example (d) labels added at random
22. 22.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) testing on training data (d) photos in many lights
23. 23.True or false: “testing on training data” is bad for training a model. (a) True (b) False
24. 24.🏷️ 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
25. 25.🥭 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
26. 26.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
27. 27.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) testing on training data (d) copies of one photo
28. 28.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
29. 29.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) testing on training data (d) correct labels
30. 30.Which of these is bad for training a model? (a) checking labels twice (b) many varied examples (c) clear, sharp examples (d) copies of one photo
31. 31.🏷️ 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
32. 32.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
33. 33.True or false: “labels added at random” is good for training a model. (a) True (b) False
34. 34.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) new data for testing (d) blurry, unclear photos
35. 35.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
36. 36.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
37. 37.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
38. 38.Which of these is good for training a model? (a) only one kind of example (b) examples of every type (c) blurry, unclear photos (d) very few examples
39. 39.Which of these is bad for training a model? (a) testing on training data (b) photos in many lights (c) many varied examples (d) clear, sharp examples
40. 40.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) slower to switch on (d) forget everything

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