Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 500 · 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 “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
2. 2.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) correct labels (d) labels added at random
3. 3.🔍 Which is the odd one out? (a) testing on training data (b) correct labels (c) many varied examples (d) clear, sharp examples
4. 4.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
5. 5.🔍 Which is the odd one out? (a) labels added at random (b) checking labels twice (c) clear, sharp examples (d) new data for testing
6. 6.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) very few examples (d) correct labels
7. 7.Which of these is bad for training a model? (a) many varied examples (b) only one kind of example (c) photos in many lights (d) checking labels twice
8. 8.True or false: “correct labels” is bad for training a model. (a) False (b) True
9. 9.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) only one kind of example (d) examples of every type
10. 10.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) blurry, unclear photos (d) new data for testing
11. 11.🏷️ 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
12. 12.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
13. 13.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
14. 14.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
15. 15.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) the right answer tag (d) a sticker on a laptop
16. 16.🏷️ 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
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
18. 18.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) slower to switch on (d) change colour
19. 19.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) very few examples (d) copies of one photo
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (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? “Send your password to claim a prize” (a) not spam (b) spam
22. 22.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) a list of rules only (d) many labelled photos
23. 23.True or false: “examples of every type” is good for training a model. (a) False (b) True
24. 24.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
25. 25.True or false: “photos in many lights” is good for training a model. (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) blurry, unclear photos (b) wrong labels (c) photos in many lights (d) only one kind of example
27. 27.🏷️ 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
28. 28.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) many varied examples (d) only one kind of example
30. 30.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) many varied examples (d) wrong labels
31. 31.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
32. 32.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) wrong labels (d) examples of every type
33. 33.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
34. 34.🏷️ 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
35. 35.True or false: “many varied examples” is bad for training a model. (a) True (b) False
36. 36.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will turn yellow (d) it will be perfect
37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
38. 38.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) clean screens (d) charge faster
39. 39.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) correct labels (d) wrong labels
40. 40.🏷️ 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

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