Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 169 · 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 “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
2. 2.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
3. 3.🏷️ 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
4. 4.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) many varied examples (d) only one kind of example
5. 5.🏷️ 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
6. 6.Which of these is good for training a model? (a) only one kind of example (b) labels added at random (c) copies of one photo (d) checking labels twice
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
8. 8.🏷️ 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
9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
10. 10.🥭 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 will turn yellow (d) it may get it wrong
11. 11.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
12. 12.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
13. 13.🔍 Which is the odd one out? (a) new data for testing (b) labels added at random (c) copies of one photo (d) very few examples
14. 14.True or false: “checking labels twice” is good for training a model. (a) False (b) True
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.True or false: “new data for testing” is bad for training a model. (a) False (b) True
17. 17.True or false: “only one kind of example” is good for training a model. (a) True (b) False
18. 18.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
19. 19.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
21. 21.Which of these is bad for training a model? (a) examples of every type (b) checking labels twice (c) new data for testing (d) wrong labels
22. 22.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
23. 23.True or false: “correct labels” is good for training a model. (a) True (b) False
24. 24.🏷️ 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
25. 25.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
26. 26.True or false: “wrong labels” is bad for training a model. (a) False (b) True
27. 27.Which of these is good for training a model? (a) clear, sharp examples (b) wrong labels (c) very few examples (d) testing on training data
28. 28.🔍 Which is the odd one out? (a) checking labels twice (b) testing on training data (c) clear, sharp examples (d) correct labels
29. 29.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) new data for testing (d) very few examples
30. 30.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
31. 31.True or false: “many varied examples” is good for training a model. (a) True (b) False
32. 32.Which of these is good for training a model? (a) examples of every type (b) only one kind of example (c) copies of one photo (d) very few examples
33. 33.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) labels added at random (d) correct labels
34. 34.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
35. 35.🏷️ 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
36. 36.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) copies of one photo (d) many varied examples
37. 37.True or false: “many varied examples” is bad for training a model. (a) True (b) False
38. 38.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
39. 39.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) examples of every type (d) very few examples
40. 40.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training

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