Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 539 · 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.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
2. 2.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
3. 3.True or false: “new data for testing” is bad for training a model. (a) False (b) True
4. 4.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
5. 5.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
6. 6.🏷️ 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
7. 7.🔍 Which is the odd one out? (a) very few examples (b) clear, sharp examples (c) wrong labels (d) copies of one photo
8. 8.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
9. 9.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on
10. 10.True or false: “new data for testing” is good for training a model. (a) True (b) False
11. 11.True or false: “labels added at random” is bad for training a model. (a) False (b) True
12. 12.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
13. 13.🔍 Which is the odd one out? (a) copies of one photo (b) correct labels (c) checking labels twice (d) new data for testing
14. 14.Which of these is bad for training a model? (a) checking labels twice (b) labels added at random (c) examples of every type (d) new data for testing
15. 15.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
16. 16.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
17. 17.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
18. 18.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) very few examples (d) new data for testing
19. 19.True or false: “correct labels” is good for training a model. (a) False (b) True
20. 20.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) copies of one photo (d) many varied examples
21. 21.🏷️ 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
22. 22.🔍 Which is the odd one out? (a) copies of one photo (b) labels added at random (c) testing on training data (d) correct labels
23. 23.🔍 Which is the odd one out? (a) many varied examples (b) wrong labels (c) only one kind of example (d) very few examples
24. 24.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
25. 25.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
26. 26.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
27. 27.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
28. 28.🏷️ 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
29. 29.Which of these is good for training a model? (a) only one kind of example (b) checking labels twice (c) labels added at random (d) blurry, unclear photos
30. 30.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
31. 31.🔍 Which is the odd one out? (a) wrong labels (b) copies of one photo (c) new data for testing (d) blurry, unclear photos
32. 32.Which of these is good for training a model? (a) blurry, unclear photos (b) only one kind of example (c) clear, sharp examples (d) labels added at random
33. 33.True or false: “examples of every type” is good for training a model. (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) many varied examples (b) only one kind of example (c) photos in many lights (d) new data for testing
35. 35.🔍 Which is the odd one out? (a) copies of one photo (b) photos in many lights (c) blurry, unclear photos (d) only one kind of example
36. 36.Which of these is bad for training a model? (a) photos in many lights (b) copies of one photo (c) new data for testing (d) clear, sharp examples
37. 37.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
38. 38.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) only one kind of example (d) examples of every type
39. 39.🏷️ 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
40. 40.Which of these is bad for training a model? (a) many varied examples (b) wrong labels (c) examples of every type (d) checking labels twice

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