Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 368 · 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.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
2. 2.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
3. 3.🔍 Which is the odd one out? (a) only one kind of example (b) very few examples (c) blurry, unclear photos (d) examples of every type
4. 4.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) examples of every type (d) new data for testing
5. 5.True or false: “wrong labels” is good for training a model. (a) False (b) True
6. 6.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a list of rules only (d) a song about dogs
7. 7.Which of these is bad for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) new data for testing (d) examples of every type
8. 8.🔍 Which is the odd one out? (a) only one kind of example (b) photos in many lights (c) very few examples (d) testing on training data
9. 9.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
10. 10.True or false: “only one kind of example” is good for training a model. (a) False (b) True
11. 11.🥭 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
12. 12.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
13. 13.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
14. 14.🔍 Which is the odd one out? (a) new data for testing (b) wrong labels (c) only one kind of example (d) labels added at random
15. 15.Which of these is bad for training a model? (a) correct labels (b) wrong labels (c) photos in many lights (d) clear, sharp examples
16. 16.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
17. 17.Which of these is good for training a model? (a) clear, sharp examples (b) copies of one photo (c) only one kind of example (d) testing on training data
18. 18.Which of these is good for training a model? (a) examples of every type (b) only one kind of example (c) blurry, unclear photos (d) labels added at random
19. 19.🏷️ 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
20. 20.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the price (d) the right answer tag
21. 21.Which of these is good for training a model? (a) many varied examples (b) wrong labels (c) very few examples (d) blurry, unclear photos
22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
23. 23.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
24. 24.🔍 Which is the odd one out? (a) very few examples (b) correct labels (c) new data for testing (d) checking labels twice
25. 25.🏷️ 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
26. 26.True or false: “examples of every type” is bad for training a model. (a) False (b) True
27. 27.Which of these is good for training a model? (a) very few examples (b) checking labels twice (c) labels added at random (d) testing on training data
28. 28.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
29. 29.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
30. 30.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
31. 31.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
32. 32.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
33. 33.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
34. 34.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
35. 35.🔍 Which is the odd one out? (a) checking labels twice (b) photos in many lights (c) clear, sharp examples (d) wrong labels
36. 36.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
37. 37.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
38. 38.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
39. 39.🏷️ 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
40. 40.True or false: “many varied examples” is bad for training a model. (a) True (b) False

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