Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 532 · 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.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) a song about dogs (d) one single photo
2. 2.🏷️ 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
3. 3.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
4. 4.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
5. 5.🏷️ 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
6. 6.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on
7. 7.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
8. 8.Which of these is good for training a model? (a) clear, sharp examples (b) only one kind of example (c) copies of one photo (d) labels added at random
9. 9.Which of these is bad for training a model? (a) testing on training data (b) photos in many lights (c) checking labels twice (d) many varied examples
10. 10.🔍 Which is the odd one out? (a) very few examples (b) photos in many lights (c) checking labels twice (d) many varied examples
11. 11.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) examples of every type (d) new data for testing
12. 12.Which of these is bad for training a model? (a) photos in many lights (b) new data for testing (c) very few examples (d) clear, sharp examples
13. 13.🏷️ 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
14. 14.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
15. 15.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
16. 16.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
17. 17.True or false: “very few examples” is good for training a model. (a) True (b) False
18. 18.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) examples of every type (d) labels added at random
19. 19.True or false: “many varied examples” is bad for training a model. (a) True (b) False
20. 20.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) only one kind of example (d) checking labels twice
21. 21.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
22. 22.🔍 Which is the odd one out? (a) new data for testing (b) wrong labels (c) very few examples (d) blurry, unclear photos
23. 23.🔍 Which is the odd one out? (a) very few examples (b) correct labels (c) wrong labels (d) labels added at random
24. 24.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
25. 25.🥭 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
26. 26.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
27. 27.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) wrong labels (d) correct labels
28. 28.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
29. 29.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
30. 30.🏷️ 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
31. 31.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
32. 32.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
33. 33.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) a sticker on a laptop (d) the right answer tag
34. 34.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
35. 35.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) testing on training data (d) clear, sharp examples
36. 36.Which of these is bad for training a model? (a) blurry, unclear photos (b) new data for testing (c) correct labels (d) examples of every type
37. 37.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) checking labels twice (d) wrong labels
38. 38.True or false: “new data for testing” is bad for training a model. (a) True (b) False
39. 39.🔍 Which is the odd one out? (a) only one kind of example (b) many varied examples (c) correct labels (d) photos in many lights
40. 40.Which of these is good for training a model? (a) wrong labels (b) copies of one photo (c) testing on training data (d) photos in many lights

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