Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 34 · 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.🏷️ 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
2. 2.🔍 Which is the odd one out? (a) labels added at random (b) correct labels (c) photos in many lights (d) checking labels twice
3. 3.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) photos in many lights (d) only one kind of example
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) a song about dogs (d) many labelled photos
5. 5.🏷️ 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
6. 6.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
8. 8.🔍 Which is the odd one out? (a) only one kind of example (b) examples of every type (c) clear, sharp examples (d) many varied examples
9. 9.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will shut down (d) it will turn yellow
10. 10.🔍 Which is the odd one out? (a) very few examples (b) photos in many lights (c) checking labels twice (d) examples of every type
11. 11.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) blurry, unclear photos (d) checking labels twice
12. 12.🏷️ 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
13. 13.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) wrong labels (d) many varied examples
14. 14.🔍 Which is the odd one out? (a) wrong labels (b) checking labels twice (c) new data for testing (d) clear, sharp examples
15. 15.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) only one kind of example (d) labels added at random
16. 16.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
17. 17.🔍 Which is the odd one out? (a) testing on training data (b) checking labels twice (c) examples of every type (d) correct labels
18. 18.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
19. 19.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
20. 20.True or false: “examples of every type” is bad for training a model. (a) False (b) True
21. 21.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
22. 22.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) examples of every type (d) only one kind of example
23. 23.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
24. 24.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
25. 25.True or false: “checking labels twice” is good for training a model. (a) True (b) False
26. 26.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
27. 27.🏷️ 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
28. 28.Which of these is bad for training a model? (a) correct labels (b) very few examples (c) photos in many lights (d) clear, sharp examples
29. 29.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) testing on training data (d) checking labels twice
30. 30.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
31. 31.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) clear, sharp examples (d) only one kind of example
32. 32.Which of these is bad for training a model? (a) copies of one photo (b) new data for testing (c) many varied examples (d) checking labels twice
33. 33.Which of these is bad for training a model? (a) photos in many lights (b) testing on training data (c) clear, sharp examples (d) checking labels twice
34. 34.True or false: “testing on training data” is good for training a model. (a) False (b) True
35. 35.True or false: “correct labels” is good for training a model. (a) True (b) False
36. 36.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
37. 37.🔍 Which is the odd one out? (a) copies of one photo (b) labels added at random (c) only one kind of example (d) clear, sharp examples
38. 38.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) examples of every type (d) copies of one photo
39. 39.🔍 Which is the odd one out? (a) blurry, unclear photos (b) many varied examples (c) testing on training data (d) labels added at random
40. 40.True or false: “checking labels twice” is bad for training a model. (a) True (b) False

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