Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 449 · 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) very few examples (b) only one kind of example (c) copies of one photo (d) examples of every type
2. 2.True or false: “very few examples” is bad for training a model. (a) True (b) False
3. 3.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) copies of one photo (d) wrong labels
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
5. 5.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
6. 6.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
7. 7.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the font size (d) the price
8. 8.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
9. 9.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) blurry, unclear photos (d) many varied examples
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
11. 11.True or false: “only one kind of example” is good for training a model. (a) True (b) False
12. 12.🏷️ 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
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) correct labels (d) very few examples
14. 14.🔍 Which is the odd one out? (a) clear, sharp examples (b) photos in many lights (c) checking labels twice (d) very few examples
15. 15.🏷️ 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
16. 16.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
17. 17.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
18. 18.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) copies of one photo (d) correct labels
20. 20.Which of these is bad for training a model? (a) blurry, unclear photos (b) many varied examples (c) clear, sharp examples (d) photos in many lights
21. 21.True or false: “wrong labels” is good for training a model. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) checking labels twice (d) many varied examples
23. 23.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it may get it wrong (d) it will turn yellow
24. 24.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
25. 25.🔍 Which is the odd one out? (a) wrong labels (b) examples of every type (c) new data for testing (d) correct labels
26. 26.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
27. 27.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) clear, sharp examples (d) testing on training data
28. 28.True or false: “photos in many lights” is good for training a model. (a) True (b) False
29. 29.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) photos in many lights (d) wrong labels
30. 30.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
31. 31.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
32. 32.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) print pages (d) charge faster
33. 33.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
34. 34.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
35. 35.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
36. 36.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) blurry, unclear photos (d) only one kind of example
37. 37.Which of these is bad for training a model? (a) correct labels (b) only one kind of example (c) examples of every type (d) photos in many lights
38. 38.Which of these is good for training a model? (a) only one kind of example (b) correct labels (c) wrong labels (d) labels added at random
39. 39.🔍 Which is the odd one out? (a) clear, sharp examples (b) only one kind of example (c) checking labels twice (d) photos in many lights
40. 40.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True

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