Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 128 · 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.True or false: “testing on training data” is good for training a model. (a) True (b) False
2. 2.Which of these is bad for training a model? (a) wrong labels (b) new data for testing (c) correct labels (d) checking labels twice
3. 3.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
4. 4.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) many varied examples (d) copies of one photo
5. 5.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
6. 6.True or false: “many varied examples” is good for training a model. (a) True (b) False
7. 7.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
8. 8.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) new data for testing (d) only one kind of example
9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
11. 11.Which of these is bad for training a model? (a) blurry, unclear photos (b) photos in many lights (c) examples of every type (d) correct labels
12. 12.True or false: “very few examples” is good for training a model. (a) True (b) False
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
14. 14.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
15. 15.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it will be perfect (d) it may get it wrong
16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
17. 17.True or false: “new data for testing” is bad for training a model. (a) False (b) True
18. 18.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) only one kind of example (d) correct labels
20. 20.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
21. 21.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
22. 22.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
23. 23.True or false: “only one kind of example” is good for training a model. (a) True (b) False
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
25. 25.Which of these is bad for training a model? (a) photos in many lights (b) labels added at random (c) clear, sharp examples (d) checking labels twice
26. 26.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
27. 27.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) labels added at random (d) examples of every type
28. 28.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
29. 29.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
30. 30.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) correct labels (b) clear, sharp examples (c) photos in many lights (d) labels added at random
32. 32.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
33. 33.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) copies of one photo (d) checking labels twice
34. 34.🔍 Which is the odd one out? (a) blurry, unclear photos (b) clear, sharp examples (c) wrong labels (d) testing on training data
35. 35.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
36. 36.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) labels added at random (d) clear, sharp examples
37. 37.Which of these is bad for training a model? (a) only one kind of example (b) checking labels twice (c) many varied examples (d) new data for testing
38. 38.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
39. 39.🏷️ 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
40. 40.True or false: “checking labels twice” is good for training a model. (a) False (b) True

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