Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 198 · 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.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
2. 2.True or false: “examples of every type” is good for training a model. (a) False (b) True
3. 3.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
4. 4.🏷️ 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
5. 5.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) copies of one photo (d) photos in many lights
6. 6.🔍 Which is the odd one out? (a) clear, sharp examples (b) testing on training data (c) checking labels twice (d) correct labels
7. 7.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
8. 8.Which of these is good for training a model? (a) copies of one photo (b) clear, sharp examples (c) very few examples (d) wrong labels
9. 9.True or false: “copies of one photo” is good for training a model. (a) True (b) False
10. 10.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
11. 11.🏷️ 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
12. 12.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) labels added at random (d) clear, sharp examples
13. 13.🏷️ 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
14. 14.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
15. 15.True or false: “many varied examples” is good for training a model. (a) False (b) True
16. 16.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
17. 17.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) wrong labels (d) new data for testing
18. 18.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
19. 19.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
20. 20.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) only one kind of example (d) new data for testing
21. 21.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
22. 22.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
23. 23.True or false: “new data for testing” is bad for training a model. (a) True (b) False
24. 24.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
25. 25.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) new data for testing (d) wrong labels
26. 26.Which of these is good for training a model? (a) testing on training data (b) photos in many lights (c) copies of one photo (d) very few examples
27. 27.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
28. 28.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) blurry, unclear photos (d) examples of every type
29. 29.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will shut down (d) it will be perfect
31. 31.🔍 Which is the odd one out? (a) clear, sharp examples (b) photos in many lights (c) labels added at random (d) new data for testing
32. 32.🔍 Which is the odd one out? (a) photos in many lights (b) wrong labels (c) correct labels (d) new data for testing
33. 33.📚 Usually, more good examples make a model… (a) better at its task (b) slower to switch on (c) forget everything (d) change colour
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.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
36. 36.Which of these is good for training a model? (a) new data for testing (b) copies of one photo (c) testing on training data (d) wrong labels
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.🏷️ 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
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
40. 40.True or false: “very few examples” is good for training a model. (a) False (b) True

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