Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 351 · 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: very few examples? (a) good for training (b) bad for training
2. 2.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
3. 3.Which of these is bad for training a model? (a) correct labels (b) labels added at random (c) many varied examples (d) checking labels twice
4. 4.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) examples of every type (d) labels added at random
5. 5.🏷️ 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
6. 6.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
7. 7.True or false: “examples of every type” is bad for training a model. (a) True (b) False
8. 8.True or false: “correct labels” is bad for training a model. (a) False (b) True
9. 9.🏷️ 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
10. 10.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) only one kind of example (d) testing on training data
11. 11.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
12. 12.🏷️ 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
13. 13.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
14. 14.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) photos in many lights (d) very few examples
15. 15.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
16. 16.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) very few examples (d) photos in many lights
17. 17.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) many labelled photos (d) a list of rules only
18. 18.True or false: “many varied examples” is good for training a model. (a) False (b) True
19. 19.Which of these is bad for training a model? (a) clear, sharp examples (b) copies of one photo (c) correct labels (d) photos in many lights
20. 20.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) labels added at random (d) clear, sharp examples
21. 21.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
22. 22.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) copies of one photo (d) correct labels
23. 23.🏷️ 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
24. 24.🏷️ 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
25. 25.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) new data for testing (d) many varied examples
26. 26.Which of these is good for training a model? (a) labels added at random (b) copies of one photo (c) blurry, unclear photos (d) many varied examples
27. 27.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) clear, sharp examples (d) wrong labels
28. 28.True or false: “testing on training data” is good for training a model. (a) True (b) False
29. 29.True or false: “photos in many lights” is good for training a model. (a) True (b) False
30. 30.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
31. 31.🔍 Which is the odd one out? (a) copies of one photo (b) new data for testing (c) correct labels (d) checking labels twice
32. 32.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
33. 33.🥭 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
34. 34.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
35. 35.True or false: “examples of every type” is good for training a model. (a) False (b) True
36. 36.True or false: “very few examples” is bad for training a model. (a) False (b) True
37. 37.🏷️ 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
38. 38.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
39. 39.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) photos in many lights (d) blurry, unclear photos
40. 40.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training

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