Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 574 · 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 is the odd one out? (a) photos in many lights (b) checking labels twice (c) very few examples (d) clear, sharp examples
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
3. 3.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
4. 4.Which of these is good for training a model? (a) correct labels (b) only one kind of example (c) wrong labels (d) blurry, unclear photos
5. 5.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
6. 6.🥭 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 turn yellow (d) it will be perfect
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.Which of these is bad for training a model? (a) new data for testing (b) clear, sharp examples (c) examples of every type (d) testing on training data
9. 9.Which of these is bad for training a model? (a) new data for testing (b) checking labels twice (c) correct labels (d) very few examples
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) many labelled photos (c) a list of rules only (d) one single photo
11. 11.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) many varied examples (d) examples of every type
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
13. 13.True or false: “very few examples” is good for training a model. (a) True (b) False
14. 14.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) correct labels (d) only one kind of example
15. 15.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) examples of every type (d) labels added at random
16. 16.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
17. 17.True or false: “copies of one photo” is good for training a model. (a) True (b) False
18. 18.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
19. 19.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
20. 20.🔍 Which is the odd one out? (a) only one kind of example (b) copies of one photo (c) new data for testing (d) very few examples
21. 21.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
22. 22.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
23. 23.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
24. 24.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) labels added at random (d) very few examples
25. 25.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
26. 26.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) very few examples (d) clear, sharp examples
27. 27.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
28. 28.🔍 Which is the odd one out? (a) only one kind of example (b) checking labels twice (c) examples of every type (d) correct labels
29. 29.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
30. 30.Which of these is bad for training a model? (a) wrong labels (b) examples of every type (c) many varied examples (d) clear, sharp examples
31. 31.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
32. 32.True or false: “new data for testing” is bad for training a model. (a) True (b) False
33. 33.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) testing on training data (d) checking labels twice
34. 34.True or false: “wrong labels” is bad for training a model. (a) False (b) True
35. 35.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) slower to switch on (d) better at its task
36. 36.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
37. 37.🔍 Which is the odd one out? (a) blurry, unclear photos (b) checking labels twice (c) new data for testing (d) clear, sharp examples
38. 38.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
39. 39.True or false: “correct labels” is bad for training a model. (a) True (b) False
40. 40.True or false: “only one kind of example” is good for training a model. (a) False (b) True

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