Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 514 · 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.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
2. 2.True or false: “examples of every type” is bad for training a model. (a) False (b) True
3. 3.🔍 Which is the odd one out? (a) testing on training data (b) many varied examples (c) photos in many lights (d) new data for testing
4. 4.🥭 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 be perfect (d) it will turn yellow
5. 5.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
6. 6.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
7. 7.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
8. 8.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
9. 9.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
10. 10.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
11. 11.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
12. 12.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
13. 13.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
14. 14.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
15. 15.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
16. 16.🔍 Which is the odd one out? (a) checking labels twice (b) labels added at random (c) clear, sharp examples (d) correct labels
17. 17.🏷️ 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
18. 18.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) checking labels twice (d) examples of every type
19. 19.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
20. 20.Which of these is bad for training a model? (a) clear, sharp examples (b) checking labels twice (c) copies of one photo (d) new data for testing
21. 21.True or false: “wrong labels” is bad for training a model. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) checking labels twice (d) very few examples
23. 23.True or false: “new data for testing” is bad for training a model. (a) True (b) False
24. 24.True or false: “only one kind of example” is good for training a model. (a) True (b) False
25. 25.True or false: “very few examples” is good for training a model. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) clear, sharp examples (d) copies of one photo
27. 27.Which of these is good for training a model? (a) very few examples (b) new data for testing (c) copies of one photo (d) blurry, unclear photos
28. 28.Which of these is good for training a model? (a) checking labels twice (b) very few examples (c) copies of one photo (d) wrong labels
29. 29.Which of these is bad for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) new data for testing (d) examples of every type
30. 30.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
31. 31.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) photos in many lights (d) labels added at random
32. 32.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) examples of every type
33. 33.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) many varied examples (d) very few examples
34. 34.🏷️ 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
35. 35.Which of these is good for training a model? (a) correct labels (b) only one kind of example (c) labels added at random (d) copies of one photo
36. 36.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
37. 37.🔍 Which is the odd one out? (a) very few examples (b) clear, sharp examples (c) blurry, unclear photos (d) labels added at random
38. 38.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
39. 39.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) clean screens (d) print pages
40. 40.🏷️ 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

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