Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 221 · 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: correct labels? (a) bad for training (b) good for training
2. 2.🔍 Which is the odd one out? (a) examples of every type (b) blurry, unclear photos (c) testing on training data (d) only one kind of example
3. 3.Which of these is bad for training a model? (a) photos in many lights (b) testing on training data (c) checking labels twice (d) many varied examples
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
5. 5.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
6. 6.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
9. 9.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) new data for testing (d) very few examples
10. 10.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the font size (d) the right answer tag
11. 11.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
12. 12.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) testing on training data (d) correct labels
13. 13.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
14. 14.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
15. 15.Which of these is good for training a model? (a) copies of one photo (b) many varied examples (c) wrong labels (d) very few examples
16. 16.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
17. 17.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
18. 18.Which of these is bad for training a model? (a) photos in many lights (b) copies of one photo (c) checking labels twice (d) correct labels
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
20. 20.True or false: “only one kind of example” is good for training a model. (a) False (b) True
21. 21.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) many varied examples (b) labels added at random (c) correct labels (d) clear, sharp examples
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
24. 24.True or false: “new data for testing” is bad for training a model. (a) True (b) False
25. 25.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
26. 26.🏷️ 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
27. 27.Which of these is bad for training a model? (a) examples of every type (b) checking labels twice (c) photos in many lights (d) wrong 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.🔍 Which is the odd one out? (a) labels added at random (b) many varied examples (c) testing on training data (d) wrong labels
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it will turn yellow (d) it may get it wrong
31. 31.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
32. 32.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
33. 33.🏷️ 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
34. 34.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) slower to switch on (d) better at its task
35. 35.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
36. 36.Which of these is bad for training a model? (a) many varied examples (b) blurry, unclear photos (c) correct labels (d) new data for testing
37. 37.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) photos in many lights (d) very few examples
38. 38.Which of these is good for training a model? (a) clear, sharp examples (b) labels added at random (c) wrong labels (d) testing on training data
39. 39.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
40. 40.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) photos in many lights (d) new data for testing

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