Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 279 · 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.🏷️ 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
2. 2.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) many varied examples (d) only one kind of example
3. 3.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
4. 4.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) only one kind of example (d) correct labels
5. 5.True or false: “testing on training data” is bad for training a model. (a) False (b) True
6. 6.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
7. 7.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) a song about dogs (d) one single photo
8. 8.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
9. 9.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) many varied examples (d) testing on training data
11. 11.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will shut down (d) it will be perfect
12. 12.True or false: “photos in many lights” is good for training a model. (a) True (b) False
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) very few examples (b) blurry, unclear photos (c) many varied examples (d) copies of one photo
15. 15.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
16. 16.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
17. 17.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) very few examples (d) copies of one photo
19. 19.Which of these is good for training a model? (a) very few examples (b) photos in many lights (c) wrong labels (d) blurry, unclear photos
20. 20.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the font size (d) the price
21. 21.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
22. 22.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
23. 23.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) testing on training data (d) blurry, unclear photos
24. 24.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
25. 25.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
26. 26.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) testing on training data (d) clear, sharp examples
27. 27.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) very few examples (d) wrong labels
28. 28.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
29. 29.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
31. 31.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
32. 32.🔍 Which is the odd one out? (a) only one kind of example (b) checking labels twice (c) blurry, unclear photos (d) very few examples
33. 33.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
34. 34.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
35. 35.🔍 Which is the odd one out? (a) new data for testing (b) blurry, unclear photos (c) many varied examples (d) checking labels twice
36. 36.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) correct labels (d) wrong labels
37. 37.True or false: “very few examples” is good for training a model. (a) True (b) False
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
39. 39.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
40. 40.🏷️ 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

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