Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 216 · 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) many labelled photos (b) one single photo (c) a song about dogs (d) a list of rules only
2. 2.True or false: “wrong labels” is bad for training a model. (a) True (b) False
3. 3.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
4. 4.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
6. 6.True or false: “only one kind of example” is good for training a model. (a) False (b) True
7. 7.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
8. 8.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) correct labels (d) new data for testing
9. 9.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
10. 10.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
11. 11.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) blurry, unclear photos (d) clear, sharp examples
12. 12.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) copies of one photo (d) photos in many lights
13. 13.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
14. 14.🥭 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
15. 15.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
16. 16.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) only one kind of example (d) correct labels
17. 17.True or false: “correct labels” is good for training a model. (a) False (b) True
18. 18.🏷️ What is a “label” in machine learning? (a) the font size (b) a sticker on a laptop (c) the right answer tag (d) the price
19. 19.🔍 Which is the odd one out? (a) checking labels twice (b) very few examples (c) copies of one photo (d) testing on training data
20. 20.Which of these is good for training a model? (a) wrong labels (b) many varied examples (c) only one kind of example (d) labels added at random
21. 21.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) copies of one photo (d) many varied examples
22. 22.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
23. 23.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
24. 24.🏷️ 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
25. 25.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
26. 26.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) learn from data (d) print pages
27. 27.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
28. 28.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
29. 29.Which of these is good for training a model? (a) checking labels twice (b) only one kind of example (c) very few examples (d) labels added at random
30. 30.🏷️ 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
31. 31.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
32. 32.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
33. 33.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
34. 34.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
35. 35.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
36. 36.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
37. 37.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) correct labels (d) very few examples
38. 38.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
39. 39.True or false: “wrong labels” is good for training a model. (a) False (b) True
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam

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