Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 83 · 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 “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
2. 2.True or false: “new data for testing” is good for training a model. (a) False (b) True
3. 3.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
4. 4.True or false: “testing on training data” is bad for training a model. (a) False (b) True
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
6. 6.True or false: “photos in many lights” is good for training a model. (a) True (b) False
7. 7.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
8. 8.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) photos in many lights (d) very few examples
9. 9.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
10. 10.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) correct labels (d) wrong labels
11. 11.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) testing on training data (b) examples of every type (c) clear, sharp examples (d) new data for testing
13. 13.True or false: “very few examples” is good for training a model. (a) True (b) False
14. 14.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
15. 15.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
16. 16.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
17. 17.🏷️ 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
18. 18.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
19. 19.Which of these is bad for training a model? (a) wrong labels (b) many varied examples (c) correct labels (d) examples of every type
20. 20.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) checking labels twice (d) labels added at random
21. 21.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
22. 22.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
23. 23.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
24. 24.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on
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 is the odd one out? (a) very few examples (b) many varied examples (c) correct labels (d) examples of every type
27. 27.Which of these is good for training a model? (a) labels added at random (b) checking labels twice (c) wrong labels (d) only one kind of example
28. 28.Which of these is bad for training a model? (a) correct labels (b) blurry, unclear photos (c) new data for testing (d) photos in many lights
29. 29.🏷️ 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
30. 30.🐶 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) one single photo (d) a song about dogs
31. 31.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will shut down (d) it will turn yellow
32. 32.Which of these is bad for training a model? (a) labels added at random (b) clear, sharp examples (c) photos in many lights (d) many varied examples
33. 33.🏷️ 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
34. 34.True or false: “wrong labels” is good for training a model. (a) True (b) False
35. 35.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) new data for testing (d) blurry, unclear photos
36. 36.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
37. 37.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
38. 38.Which of these is good for training a model? (a) new data for testing (b) blurry, unclear photos (c) testing on training data (d) wrong labels
39. 39.Which of these is good for training a model? (a) blurry, unclear photos (b) only one kind of example (c) copies of one photo (d) clear, sharp examples
40. 40.🏷️ 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

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