Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 511 · 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: blurry, unclear photos? (a) bad for training (b) good for training
2. 2.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
3. 3.Which of these is good for training a model? (a) copies of one photo (b) many varied examples (c) blurry, unclear photos (d) only one kind of example
4. 4.True or false: “labels added at random” is good for training a model. (a) False (b) True
5. 5.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
8. 8.🏷️ 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
9. 9.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
10. 10.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) checking labels twice (d) testing on training data
11. 11.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
12. 12.🏷️ 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
13. 13.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) wrong labels (d) clear, sharp examples
14. 14.True or false: “examples of every type” is bad for training a model. (a) True (b) False
15. 15.🥭 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 be perfect (d) it will turn yellow
16. 16.🏷️ 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) not spam (b) spam
17. 17.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
18. 18.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) new data for testing (d) only one kind of example
19. 19.Which of these is bad for training a model? (a) photos in many lights (b) copies of one photo (c) checking labels twice (d) new data for testing
20. 20.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) wrong labels (d) new data for testing
21. 21.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
22. 22.🏷️ 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
23. 23.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
24. 24.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
25. 25.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) many varied examples (d) wrong labels
26. 26.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) correct labels (d) new data for testing
27. 27.🔍 Which is the odd one out? (a) copies of one photo (b) wrong labels (c) examples of every type (d) very few examples
28. 28.🏷️ 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
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) very few examples (d) correct labels
30. 30.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
31. 31.🔍 Which is the odd one out? (a) correct labels (b) examples of every type (c) only one kind of example (d) photos in many lights
32. 32.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
33. 33.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
35. 35.Which of these is bad for training a model? (a) clear, sharp examples (b) checking labels twice (c) correct labels (d) only one kind of example
36. 36.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) photos in many lights (d) testing on training data
37. 37.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) better at its task (d) forget everything
38. 38.🔍 Which is the odd one out? (a) wrong labels (b) labels added at random (c) testing on training data (d) correct labels
39. 39.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) many labelled photos (d) one single photo
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam

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