Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 495 · 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.🐶 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
2. 2.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) very few examples (d) blurry, unclear photos
3. 3.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) labels added at random (d) wrong labels
4. 4.🏷️ 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
5. 5.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
6. 6.🏷️ 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
7. 7.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) photos in many lights (d) wrong labels
8. 8.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) new data for testing (d) very few examples
9. 9.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) new data for testing (d) wrong labels
10. 10.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) clear, sharp examples (d) wrong labels
11. 11.True or false: “photos in many lights” is good for training a model. (a) True (b) False
12. 12.🏷️ 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
13. 13.True or false: “examples of every type” is good for training a model. (a) False (b) True
14. 14.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) checking labels twice (d) only one kind of example
15. 15.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
16. 16.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) correct labels (d) very few examples
17. 17.🏷️ 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
18. 18.🔍 Which is the odd one out? (a) labels added at random (b) many varied examples (c) examples of every type (d) correct labels
19. 19.Which of these is bad for training a model? (a) many varied examples (b) labels added at random (c) correct labels (d) new data for testing
20. 20.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
21. 21.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
22. 22.🔍 Which is the odd one out? (a) copies of one photo (b) new data for testing (c) examples of every type (d) many varied examples
23. 23.Which of these is bad for training a model? (a) photos in many lights (b) copies of one photo (c) correct labels (d) many varied examples
24. 24.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) better at its task (d) change colour
25. 25.True or false: “very few examples” is bad for training a model. (a) True (b) False
26. 26.True or false: “labels added at random” is good for training a model. (a) True (b) False
27. 27.Which of these is bad for training a model? (a) examples of every type (b) blurry, unclear photos (c) correct labels (d) new data for testing
28. 28.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) only one kind of example (d) checking labels twice
29. 29.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
30. 30.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
31. 31.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
32. 32.True or false: “new data for testing” is good for training a model. (a) False (b) True
33. 33.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
34. 34.🏷️ 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
35. 35.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) testing on training data (d) many varied examples
36. 36.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
37. 37.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) blurry, unclear photos (d) wrong labels
38. 38.🥭 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
39. 39.Which of these is bad for training a model? (a) correct labels (b) only one kind of example (c) photos in many lights (d) many varied examples
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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