Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 338 · 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 of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) only one kind of example (d) clear, sharp examples
2. 2.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
3. 3.True or false: “checking labels twice” is good for training a model. (a) False (b) True
4. 4.Which of these is good for training a model? (a) copies of one photo (b) many varied examples (c) labels added at random (d) only one kind of example
5. 5.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
6. 6.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) labels added at random (d) examples of every type
7. 7.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
8. 8.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
9. 9.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
10. 10.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
11. 11.🏷️ 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
12. 12.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) a list of rules only (d) many labelled photos
13. 13.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
14. 14.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
15. 15.True or false: “new data for testing” is bad for training a model. (a) False (b) True
16. 16.True or false: “labels added at random” is bad for training a model. (a) False (b) True
17. 17.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
18. 18.🏷️ 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
19. 19.True or false: “wrong labels” is good for training a model. (a) False (b) True
20. 20.True or false: “labels added at random” is good for training a model. (a) True (b) False
21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
22. 22.🏷️ 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
23. 23.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
24. 24.🏷️ 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
25. 25.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) checking labels twice (d) very few examples
26. 26.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
27. 27.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) photos in many lights (d) only one kind of example
28. 28.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) copies of one photo (d) new data for testing
29. 29.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) photos in many lights (d) many varied examples
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
31. 31.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
32. 32.🔍 Which is the odd one out? (a) wrong labels (b) testing on training data (c) correct labels (d) very few examples
33. 33.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
34. 34.True or false: “examples of every type” is bad for training a model. (a) True (b) False
35. 35.Which of these is bad for training a model? (a) new data for testing (b) blurry, unclear photos (c) photos in many lights (d) examples of every type
36. 36.Which of these is bad for training a model? (a) checking labels twice (b) examples of every type (c) correct labels (d) wrong labels
37. 37.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
38. 38.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will turn yellow (c) it will shut down (d) it may get it wrong
39. 39.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
40. 40.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) examples of every type (d) only one kind of example

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