Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 284 · 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.Which of these is good for training a model? (a) labels added at random (b) copies of one photo (c) only one kind of example (d) many varied examples
2. 2.🐶 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
3. 3.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) examples of every type (d) wrong labels
4. 4.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
5. 5.🔍 Which is the odd one out? (a) correct labels (b) wrong labels (c) copies of one photo (d) labels added at random
6. 6.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
7. 7.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
8. 8.🔍 Which is the odd one out? (a) very few examples (b) photos in many lights (c) testing on training data (d) blurry, unclear photos
9. 9.🏷️ 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
10. 10.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
11. 11.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will be perfect (c) it may get it wrong (d) it will shut down
12. 12.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
13. 13.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
14. 14.🏷️ 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
15. 15.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
16. 16.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the price (d) the right answer tag
17. 17.Which of these is bad for training a model? (a) correct labels (b) blurry, unclear photos (c) photos in many lights (d) checking labels twice
18. 18.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
19. 19.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
20. 20.🔍 Which is the odd one out? (a) very few examples (b) clear, sharp examples (c) blurry, unclear photos (d) wrong labels
21. 21.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
22. 22.Which of these is good for training a model? (a) only one kind of example (b) labels added at random (c) clear, sharp examples (d) very few examples
23. 23.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) testing on training data (d) correct labels
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
25. 25.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) copies of one photo (d) many varied examples
26. 26.Which of these is good for training a model? (a) wrong labels (b) copies of one photo (c) photos in many lights (d) very few examples
27. 27.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
28. 28.True or false: “testing on training data” is bad for training a model. (a) True (b) False
29. 29.🏷️ 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
30. 30.True or false: “examples of every type” is bad for training a model. (a) True (b) False
31. 31.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
32. 32.Which of these is good for training a model? (a) new data for testing (b) copies of one photo (c) only one kind of example (d) wrong labels
33. 33.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
34. 34.True or false: “many varied examples” is good for training a model. (a) True (b) False
35. 35.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
36. 36.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
37. 37.🏷️ 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
38. 38.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) clear, sharp examples (d) photos in many lights
39. 39.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
40. 40.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False

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