Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 135 · 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.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) new data for testing (d) blurry, unclear photos
2. 2.🏷️ 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
3. 3.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) clear, sharp examples (d) labels added at random
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
5. 5.True or false: “correct labels” is good for training a model. (a) False (b) True
6. 6.Which of these is bad for training a model? (a) photos in many lights (b) only one kind of example (c) clear, sharp examples (d) correct labels
7. 7.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) a list of rules only (d) one single photo
8. 8.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) slower to switch on (d) better at its task
9. 9.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) photos in many lights (d) blurry, unclear photos
10. 10.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
11. 11.🔍 Which is the odd one out? (a) testing on training data (b) new data for testing (c) clear, sharp examples (d) correct labels
12. 12.🔍 Which is the odd one out? (a) wrong labels (b) correct labels (c) clear, sharp examples (d) new data for testing
13. 13.Which of these is good for training a model? (a) correct labels (b) very few examples (c) labels added at random (d) blurry, unclear photos
14. 14.🔍 Which is the odd one out? (a) clear, sharp examples (b) wrong labels (c) labels added at random (d) testing on training data
15. 15.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
16. 16.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
17. 17.Which of these is good for training a model? (a) labels added at random (b) photos in many lights (c) wrong labels (d) very few examples
18. 18.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
19. 19.True or false: “wrong labels” is good for training a model. (a) False (b) True
20. 20.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
21. 21.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) labels added at random (d) new data for testing
22. 22.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
23. 23.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
24. 24.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
25. 25.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
26. 26.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
27. 27.True or false: “copies of one photo” is good for training a model. (a) False (b) True
28. 28.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) clear, sharp examples (d) correct labels
30. 30.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
31. 31.True or false: “correct labels” is bad for training a model. (a) False (b) True
32. 32.🔍 Which is the odd one out? (a) labels added at random (b) examples of every type (c) blurry, unclear photos (d) copies of one photo
33. 33.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) correct labels (d) wrong labels
34. 34.Which of these is bad for training a model? (a) photos in many lights (b) new data for testing (c) correct labels (d) testing on training data
35. 35.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
36. 36.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) many varied examples (d) copies of one photo
37. 37.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
38. 38.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
39. 39.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
40. 40.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training

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