Machine Learning for Kids Worksheet

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

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