Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 375 · 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.🏷️ 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
2. 2.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
3. 3.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
4. 4.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
6. 6.🏷️ 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
7. 7.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
8. 8.True or false: “many varied examples” is bad for training a model. (a) False (b) True
9. 9.Which of these is bad for training a model? (a) many varied examples (b) only one kind of example (c) new data for testing (d) checking labels twice
10. 10.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
11. 11.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
12. 12.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
13. 13.True or false: “only one kind of example” is good for training a model. (a) True (b) False
14. 14.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) blurry, unclear photos (d) clear, sharp examples
15. 15.🏷️ 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
16. 16.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) copies of one photo (d) checking labels twice
17. 17.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) blurry, unclear photos (d) many varied examples
18. 18.🔍 Which is the odd one out? (a) very few examples (b) examples of every type (c) blurry, unclear photos (d) wrong labels
19. 19.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
20. 20.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
21. 21.Which of these is good for training a model? (a) very few examples (b) correct labels (c) testing on training data (d) copies of one photo
22. 22.True or false: “very few examples” is bad for training a model. (a) True (b) False
23. 23.Which of these is good for training a model? (a) copies of one photo (b) new data for testing (c) very few examples (d) wrong labels
24. 24.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a song about dogs (d) a list of rules only
25. 25.True or false: “labels added at random” is bad for training a model. (a) False (b) True
26. 26.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
27. 27.Which of these is bad for training a model? (a) examples of every type (b) very few examples (c) checking labels twice (d) new data for testing
28. 28.True or false: “examples of every type” is good for training a model. (a) False (b) True
29. 29.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
30. 30.🔍 Which is the odd one out? (a) labels added at random (b) wrong labels (c) only one kind of example (d) many varied examples
31. 31.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
32. 32.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) checking labels twice (d) labels added at random
33. 33.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) wrong labels (d) new data for testing
34. 34.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
35. 35.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
36. 36.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
37. 37.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) new data for testing (d) labels added at random
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
39. 39.🏷️ 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
40. 40.🏷️ 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

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