Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 82 · 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: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
2. 2.True or false: “labels added at random” is good for training a model. (a) True (b) False
3. 3.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
4. 4.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
5. 5.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
6. 6.Which of these is bad for training a model? (a) clear, sharp examples (b) correct labels (c) photos in many lights (d) blurry, unclear photos
7. 7.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
8. 8.True or false: “correct labels” is bad for training a model. (a) True (b) False
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) a song about dogs (d) many labelled photos
10. 10.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
11. 11.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) the right answer tag (d) a sticker on a laptop
12. 12.🏷️ 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
13. 13.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) blurry, unclear photos (d) new data for testing
14. 14.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
15. 15.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) charge faster (d) print pages
16. 16.True or false: “many varied examples” is bad for training a model. (a) True (b) False
17. 17.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
18. 18.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) clear, sharp examples (d) labels added at random
19. 19.Which of these is good for training a model? (a) labels added at random (b) examples of every type (c) very few examples (d) testing on training data
20. 20.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
21. 21.Which of these is good for training a model? (a) labels added at random (b) many varied examples (c) only one kind of example (d) wrong labels
22. 22.🥭 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
23. 23.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) checking labels twice (d) copies of one photo
24. 24.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
26. 26.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) very few examples (d) correct labels
27. 27.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
28. 28.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) wrong labels (d) copies of one photo
29. 29.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
30. 30.Which of these is bad for training a model? (a) examples of every type (b) very few examples (c) checking labels twice (d) new data for testing
31. 31.🔍 Which is the odd one out? (a) checking labels twice (b) only one kind of example (c) very few examples (d) labels added at random
32. 32.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) checking labels twice (d) new data for testing
33. 33.🏷️ 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
34. 34.True or false: “wrong labels” is good for training a model. (a) True (b) False
35. 35.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
36. 36.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
37. 37.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) clear, sharp examples (d) testing on training data
38. 38.🔍 Which is the odd one out? (a) very few examples (b) wrong labels (c) clear, sharp examples (d) only one kind of example
39. 39.True or false: “new data for testing” is bad for training a model. (a) False (b) True
40. 40.True or false: “only one kind of example” is good for training a model. (a) False (b) True

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