Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 136 · 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 is the odd one out? (a) only one kind of example (b) testing on training data (c) very few examples (d) new data for testing
2. 2.🏷️ 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
3. 3.🏷️ 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
4. 4.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
5. 5.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
6. 6.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
7. 7.True or false: “testing on training data” is bad for training a model. (a) True (b) False
8. 8.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) checking labels twice (d) wrong labels
9. 9.True or false: “only one kind of example” is good for training a model. (a) False (b) True
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
11. 11.Which of these is good for training a model? (a) testing on training data (b) examples of every type (c) only one kind of example (d) very few examples
12. 12.🏷️ 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
13. 13.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) blurry, unclear photos (d) photos in many lights
14. 14.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
15. 15.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) testing on training data (d) many varied examples
16. 16.🥭 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
17. 17.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
18. 18.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
19. 19.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
20. 20.🔍 Which is the odd one out? (a) clear, sharp examples (b) very few examples (c) copies of one photo (d) blurry, unclear photos
21. 21.🔍 Which is the odd one out? (a) blurry, unclear photos (b) wrong labels (c) testing on training data (d) many varied examples
22. 22.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
23. 23.🏷️ 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
24. 24.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) learn from data (d) clean screens
25. 25.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) very few examples (d) correct labels
27. 27.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
28. 28.True or false: “labels added at random” is bad for training a model. (a) False (b) True
29. 29.🏷️ 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
30. 30.Which of these is good for training a model? (a) only one kind of example (b) clear, sharp examples (c) blurry, unclear photos (d) wrong labels
31. 31.🏷️ 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
32. 32.True or false: “very few examples” is bad for training a model. (a) False (b) True
33. 33.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) wrong labels (d) checking labels twice
34. 34.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) only one kind of example (d) examples of every type
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) not spam (b) spam
36. 36.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
37. 37.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
38. 38.🏷️ 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
39. 39.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) labels added at random (d) new data for testing
40. 40.True or false: “testing on training data” is good for training a model. (a) False (b) True

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