Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 528 · 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: wrong labels? (a) good for training (b) bad for training
2. 2.🔍 Which is the odd one out? (a) copies of one photo (b) checking labels twice (c) wrong labels (d) blurry, unclear photos
3. 3.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a list of rules only (d) a song about dogs
4. 4.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
5. 5.🔍 Which is the odd one out? (a) checking labels twice (b) new data for testing (c) photos in many lights (d) copies of one photo
6. 6.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
7. 7.🏷️ 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
8. 8.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
9. 9.🔍 Which is the odd one out? (a) copies of one photo (b) wrong labels (c) labels added at random (d) many varied examples
10. 10.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
11. 11.True or false: “examples of every type” is bad for training a model. (a) False (b) True
12. 12.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
14. 14.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
15. 15.Which of these is good for training a model? (a) photos in many lights (b) testing on training data (c) labels added at random (d) wrong labels
16. 16.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) testing on training data (d) correct labels
17. 17.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) new data for testing (d) photos in many lights
18. 18.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
19. 19.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) clear, sharp examples (d) testing on training data
20. 20.Which of these is good for training a model? (a) blurry, unclear photos (b) examples of every type (c) copies of one photo (d) only one kind of example
21. 21.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) photos in many lights (d) many varied examples
22. 22.True or false: “examples of every type” is good for training a model. (a) False (b) True
23. 23.True or false: “blurry, unclear photos” is bad 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) spam (b) not spam
25. 25.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
26. 26.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) new data for testing (d) labels added at random
27. 27.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) correct labels (d) blurry, unclear photos
28. 28.Which of these is bad for training a model? (a) copies of one photo (b) clear, sharp examples (c) new data for testing (d) many varied examples
29. 29.Which of these is bad for training a model? (a) examples of every type (b) labels added at random (c) many varied examples (d) photos in many lights
30. 30.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
31. 31.True or false: “copies of one photo” is good for training a model. (a) True (b) False
32. 32.🏷️ 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
33. 33.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) photos in many lights (d) blurry, unclear photos
34. 34.🥭 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
35. 35.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
36. 36.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
37. 37.Which of these is bad for training a model? (a) wrong labels (b) correct labels (c) photos in many lights (d) clear, sharp examples
38. 38.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
40. 40.🔍 Which is the odd one out? (a) clear, sharp examples (b) wrong labels (c) blurry, unclear photos (d) copies of one photo

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