Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 193 · 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.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
2. 2.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) correct labels (d) copies of one photo
3. 3.True or false: “only one kind of example” is good for training a model. (a) False (b) True
4. 4.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
5. 5.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
6. 6.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a song about dogs (d) a list of rules only
7. 7.🔍 Which is the odd one out? (a) testing on training data (b) only one kind of example (c) photos in many lights (d) labels added at random
8. 8.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
9. 9.🔍 Which is the odd one out? (a) testing on training data (b) examples of every type (c) very few examples (d) only one kind of example
10. 10.🥭 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
11. 11.🔍 Which is the odd one out? (a) blurry, unclear photos (b) labels added at random (c) very few examples (d) new data for testing
12. 12.True or false: “correct labels” is good for training a model. (a) True (b) False
13. 13.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) the font size (d) a sticker on a laptop
14. 14.True or false: “new data for testing” is good for training a model. (a) True (b) False
15. 15.True or false: “wrong labels” is good for training a model. (a) False (b) True
16. 16.🔍 Which is the odd one out? (a) correct labels (b) labels added at random (c) checking labels twice (d) examples of every type
17. 17.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) many varied examples (d) labels added at random
18. 18.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
19. 19.Which of these is bad for training a model? (a) checking labels twice (b) new data for testing (c) copies of one photo (d) clear, sharp examples
20. 20.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) checking labels twice (b) photos in many lights (c) examples of every type (d) only one kind of example
22. 22.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
23. 23.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
24. 24.🏷️ 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
25. 25.🔍 Which is the odd one out? (a) clear, sharp examples (b) very few examples (c) many varied examples (d) correct labels
26. 26.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
27. 27.True or false: “copies of one photo” is good for training a model. (a) False (b) True
28. 28.Which of these is good for training a model? (a) copies of one photo (b) wrong labels (c) new data for testing (d) labels added at random
29. 29.Which of these is bad for training a model? (a) very few examples (b) checking labels twice (c) clear, sharp examples (d) new data for testing
30. 30.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
31. 31.True or false: “checking labels twice” is good for training a model. (a) True (b) False
32. 32.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) photos in many lights (d) testing on training data
33. 33.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
34. 34.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
35. 35.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) wrong labels (d) correct labels
36. 36.True or false: the message “Match practice moved to 5 pm” 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 “not spam”. (a) False (b) True
38. 38.True or false: “many varied examples” is bad for training a model. (a) True (b) False
39. 39.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
40. 40.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training

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