Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 239 · 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.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) one single photo (d) many labelled photos
2. 2.True or false: “examples of every type” is bad for training a model. (a) False (b) True
3. 3.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) new data for testing (d) blurry, unclear photos
4. 4.Which of these is bad for training a model? (a) examples of every type (b) checking labels twice (c) labels added at random (d) new data for testing
5. 5.🔍 Which is the odd one out? (a) many varied examples (b) only one kind of example (c) labels added at random (d) wrong labels
6. 6.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
7. 7.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
8. 8.True or false: “many varied examples” is bad for training a model. (a) True (b) False
9. 9.True or false: “labels added at random” is good for training a model. (a) False (b) True
10. 10.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) the right answer tag (d) a sticker on a laptop
11. 11.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
12. 12.🔍 Which is the odd one out? (a) very few examples (b) checking labels twice (c) photos in many lights (d) examples of every type
13. 13.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) clear, sharp examples (d) correct labels
14. 14.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) examples of every type (d) labels added at random
15. 15.True or false: “correct labels” is bad for training a model. (a) True (b) False
16. 16.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
17. 17.🏷️ 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
18. 18.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) very few examples (d) many varied examples
19. 19.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
20. 20.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
21. 21.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
22. 22.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) labels added at random (d) clear, sharp examples
23. 23.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
24. 24.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) checking labels twice (d) only one kind of example
25. 25.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
26. 26.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
27. 27.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it may get it wrong (c) it will be perfect (d) it will shut down
28. 28.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
29. 29.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
30. 30.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
31. 31.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
32. 32.🔍 Which is the odd one out? (a) clear, sharp examples (b) wrong labels (c) testing on training data (d) only one kind of example
33. 33.🏷️ 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
34. 34.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
35. 35.🔍 Which is the odd one out? (a) labels added at random (b) correct labels (c) many varied examples (d) examples of every type
36. 36.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
37. 37.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) many varied examples (d) checking labels twice
39. 39.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
40. 40.Which of these is bad for training a model? (a) checking labels twice (b) copies of one photo (c) many varied examples (d) clear, sharp examples

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