Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 127 · 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: photos in many lights? (a) good for training (b) bad for training
2. 2.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) photos in many lights (d) only one kind of example
3. 3.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) wrong labels (d) photos in many lights
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) a list of rules only (d) many labelled photos
5. 5.🔍 Which is the odd one out? (a) blurry, unclear photos (b) many varied examples (c) photos in many lights (d) correct labels
6. 6.🥭 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
7. 7.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) examples of every type (d) very few examples
8. 8.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
9. 9.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
10. 10.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
11. 11.🔍 Which is the odd one out? (a) new data for testing (b) labels added at random (c) photos in many lights (d) clear, sharp examples
12. 12.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) examples of every type (d) wrong labels
14. 14.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
15. 15.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) blurry, unclear photos (d) many varied examples
16. 16.Which of these is bad for training a model? (a) only one kind of example (b) clear, sharp examples (c) many varied examples (d) new data for testing
17. 17.True or false: “only one kind of example” is good for training a model. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) new data for testing (b) testing on training data (c) correct labels (d) many varied examples
19. 19.Which of these is good for training a model? (a) testing on training data (b) checking labels twice (c) copies of one photo (d) very few examples
20. 20.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
21. 21.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
22. 22.🏷️ 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
23. 23.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
24. 24.True or false: “many varied examples” is bad for training a model. (a) False (b) True
25. 25.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
26. 26.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) new data for testing (d) labels added at random
27. 27.Which of these is bad for training a model? (a) checking labels twice (b) many varied examples (c) examples of every type (d) blurry, unclear photos
28. 28.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
29. 29.True or false: “very few examples” is bad for training a model. (a) False (b) True
30. 30.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
31. 31.True or false: “labels added at random” is bad for training a model. (a) True (b) False
32. 32.Which of these is good for training a model? (a) testing on training data (b) clear, sharp examples (c) blurry, unclear photos (d) labels added at random
33. 33.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
34. 34.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) clear, sharp examples (d) copies of one photo
35. 35.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
36. 36.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
37. 37.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
39. 39.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
40. 40.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) copies of one photo (d) correct labels

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