Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 444 · 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.🏷️ 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
2. 2.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
3. 3.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
5. 5.🔍 Which is the odd one out? (a) checking labels twice (b) new data for testing (c) wrong labels (d) many varied examples
6. 6.True or false: “labels added at random” is good for training a model. (a) False (b) True
7. 7.🥭 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 will shut down (d) it may get it wrong
8. 8.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
9. 9.Which of these is bad for training a model? (a) correct labels (b) wrong labels (c) clear, sharp examples (d) examples of every type
10. 10.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) correct labels (d) testing on training data
11. 11.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
12. 12.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) photos in many lights (d) wrong labels
13. 13.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) wrong labels (d) correct labels
14. 14.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
15. 15.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
17. 17.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
18. 18.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) blurry, unclear photos (d) copies of one photo
20. 20.True or false: “examples of every type” is bad for training a model. (a) True (b) False
21. 21.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) blurry, unclear photos (d) wrong labels
23. 23.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
24. 24.🏷️ 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
25. 25.Which of these is good for training a model? (a) very few examples (b) new data for testing (c) testing on training data (d) labels added at random
26. 26.🏷️ 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
27. 27.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) examples of every type (d) labels added at random
28. 28.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) blurry, unclear photos (d) very few examples
30. 30.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) slower to switch on (d) change colour
31. 31.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
32. 32.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) many varied examples (d) only one kind of example
33. 33.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) correct labels (d) only one kind of example
34. 34.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
35. 35.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
36. 36.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
37. 37.True or false: “wrong labels” is good for training a model. (a) True (b) False
38. 38.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
39. 39.🏷️ 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
40. 40.True or false: “new data for testing” is bad for training a model. (a) False (b) True

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