Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 461 · 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 “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
2. 2.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
3. 3.Which of these is good for training a model? (a) wrong labels (b) clear, sharp examples (c) only one kind of example (d) testing on training data
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) many labelled photos (d) one single photo
5. 5.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
6. 6.🔍 Which is the odd one out? (a) clear, sharp examples (b) photos in many lights (c) labels added at random (d) many varied examples
7. 7.Which of these is bad for training a model? (a) wrong labels (b) checking labels twice (c) clear, sharp examples (d) many varied examples
8. 8.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
10. 10.🏷️ 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
11. 11.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) new data for testing (d) blurry, unclear photos
12. 12.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) testing on training data (d) correct labels
13. 13.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
14. 14.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
15. 15.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
16. 16.True or false: “testing on training data” is bad for training a model. (a) True (b) False
17. 17.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) better at its task (d) forget everything
18. 18.🔍 Which is the odd one out? (a) very few examples (b) photos in many lights (c) copies of one photo (d) blurry, unclear photos
19. 19.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) checking labels twice (d) wrong labels
20. 20.Which of these is bad for training a model? (a) blurry, unclear photos (b) new data for testing (c) checking labels twice (d) clear, sharp examples
21. 21.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
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) spam (b) not spam
23. 23.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) copies of one photo (d) clear, sharp examples
24. 24.Which of these is bad for training a model? (a) very few examples (b) clear, sharp examples (c) many varied examples (d) photos in many lights
25. 25.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
26. 26.True or false: “very few examples” is good for training a model. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) correct labels (b) copies of one photo (c) many varied examples (d) checking labels twice
28. 28.🔍 Which is the odd one out? (a) wrong labels (b) very few examples (c) labels added at random (d) many varied examples
29. 29.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the font size (d) the price
30. 30.True or false: “copies of one photo” is good for training a model. (a) False (b) True
31. 31.🏷️ 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
32. 32.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) blurry, unclear photos (d) clear, sharp examples
33. 33.🏷️ 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
34. 34.🥭 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
35. 35.Which of these is good for training a model? (a) blurry, unclear photos (b) labels added at random (c) very few examples (d) many varied examples
36. 36.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
37. 37.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
38. 38.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
39. 39.🏷️ 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
40. 40.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) wrong labels (d) blurry, unclear photos

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