Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 414 · 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 “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
2. 2.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
3. 3.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will turn yellow (d) it will shut down
4. 4.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
5. 5.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
6. 6.📚 Usually, more good examples make a model… (a) better at its task (b) slower to switch on (c) forget everything (d) change colour
7. 7.Which of these is bad for training a model? (a) very few examples (b) clear, sharp examples (c) examples of every type (d) checking labels twice
8. 8.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) testing on training data (d) many varied examples
9. 9.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
10. 10.🏷️ 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
11. 11.True or false: “labels added at random” is good for training a model. (a) True (b) False
12. 12.Which of these is bad for training a model? (a) photos in many lights (b) new data for testing (c) labels added at random (d) clear, sharp examples
13. 13.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
14. 14.True or false: “examples of every type” is bad for training a model. (a) True (b) False
15. 15.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
17. 17.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
18. 18.True or false: “very few examples” is good for training a model. (a) True (b) False
19. 19.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
20. 20.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
21. 21.🏷️ 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
22. 22.🏷️ 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
23. 23.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) clear, sharp examples (d) copies of one photo
24. 24.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
25. 25.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
26. 26.🏷️ 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
27. 27.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
28. 28.🔍 Which is the odd one out? (a) labels added at random (b) examples of every type (c) many varied examples (d) new data for testing
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) copies of one photo (d) photos in many lights
30. 30.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
31. 31.🔍 Which is the odd one out? (a) labels added at random (b) testing on training data (c) examples of every type (d) wrong labels
32. 32.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) only one kind of example (d) clear, sharp examples
33. 33.🏷️ 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
34. 34.Which of these is bad for training a model? (a) checking labels twice (b) examples of every type (c) testing on training data (d) many varied examples
35. 35.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
36. 36.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
37. 37.Which of these is bad for training a model? (a) many varied examples (b) clear, sharp examples (c) checking labels twice (d) copies of one photo
38. 38.True or false: “correct labels” is bad for training a model. (a) False (b) True
39. 39.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) the font size (d) a sticker on a laptop
40. 40.🏷️ 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

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