Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 158 · 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 bad for training a model? (a) checking labels twice (b) clear, sharp examples (c) many varied examples (d) testing on training data
2. 2.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
3. 3.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
4. 4.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) blurry, unclear photos (d) photos in many lights
5. 5.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
6. 6.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
7. 7.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) slower to switch on (d) forget everything
8. 8.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) correct labels (d) copies of one photo
9. 9.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) print pages (d) clean screens
10. 10.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
11. 11.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
12. 12.Which of these is good for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) very few examples (d) wrong labels
13. 13.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) many varied examples (d) blurry, unclear photos
14. 14.🔍 Which is the odd one out? (a) new data for testing (b) blurry, unclear photos (c) examples of every type (d) correct labels
15. 15.Which of these is good for training a model? (a) only one kind of example (b) copies of one photo (c) testing on training data (d) examples of every type
16. 16.True or false: “testing on training data” is good for training a model. (a) True (b) False
17. 17.True or false: “examples of every type” is good for training a model. (a) False (b) True
18. 18.🏷️ 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
19. 19.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
20. 20.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
21. 21.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will turn yellow (c) it will shut down (d) it may get it wrong
22. 22.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
23. 23.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
24. 24.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
25. 25.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
26. 26.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) checking labels twice (d) wrong labels
27. 27.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) very few examples (d) clear, sharp examples
28. 28.🔍 Which is the odd one out? (a) checking labels twice (b) labels added at random (c) correct labels (d) new data for testing
29. 29.🏷️ 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
30. 30.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
31. 31.True or false: “very few examples” is good for training a model. (a) True (b) False
32. 32.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
33. 33.True or false: “new data for testing” is good for training a model. (a) False (b) True
34. 34.True or false: “many varied examples” is bad 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? “You won a free phone! Click now!” (a) spam (b) not spam
36. 36.🏷️ 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
37. 37.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
38. 38.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
39. 39.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
40. 40.True or false: “very few examples” is bad for training a model. (a) True (b) False

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