Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 377 · 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) clear, sharp examples (c) wrong labels (d) new data for testing
2. 2.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will shut down (d) it will turn yellow
3. 3.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) one single photo (d) a list of rules only
4. 4.🔍 Which is the odd one out? (a) labels added at random (b) many varied examples (c) checking labels twice (d) new data for testing
5. 5.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
6. 6.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
7. 7.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
8. 8.Which of these is good for training a model? (a) clear, sharp examples (b) labels added at random (c) only one kind of example (d) testing on training data
9. 9.🏷️ 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
10. 10.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
11. 11.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
12. 12.Which of these is good for training a model? (a) very few examples (b) correct labels (c) copies of one photo (d) wrong labels
13. 13.🔍 Which is the odd one out? (a) labels added at random (b) testing on training data (c) clear, sharp examples (d) very few examples
14. 14.True or false: “many varied examples” is bad for training a model. (a) True (b) False
15. 15.True or false: “photos in many lights” is good for training a model. (a) False (b) True
16. 16.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
17. 17.🏷️ 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
18. 18.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) wrong labels (d) new data for testing
19. 19.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) photos in many lights (d) only one kind of example
20. 20.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
21. 21.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
22. 22.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
23. 23.True or false: “labels added at random” 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? “Please buy milk on the way home” (a) not spam (b) spam
25. 25.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
26. 26.Which of these is good for training a model? (a) testing on training data (b) photos in many lights (c) only one kind of example (d) wrong labels
27. 27.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) blurry, unclear photos (d) photos in many lights
28. 28.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) new data for testing (d) very few examples
29. 29.🔍 Which is the odd one out? (a) testing on training data (b) examples of every type (c) correct labels (d) many varied examples
30. 30.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
31. 31.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
32. 32.True or false: “new data for testing” is bad for training a model. (a) False (b) True
33. 33.🏷️ 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
34. 34.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
35. 35.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) checking labels twice (d) copies of one photo
36. 36.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
38. 38.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
40. 40.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True

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