Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 478 · 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.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
2. 2.🏷️ 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
3. 3.Which of these is good for training a model? (a) examples of every type (b) blurry, unclear photos (c) very few examples (d) testing on training data
4. 4.Which of these is bad for training a model? (a) examples of every type (b) many varied examples (c) clear, sharp examples (d) wrong labels
5. 5.True or false: “examples of every type” is good for training a model. (a) True (b) False
6. 6.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
7. 7.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
8. 8.🏷️ What is a “label” in machine learning? (a) the font size (b) a sticker on a laptop (c) the right answer tag (d) the price
9. 9.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
10. 10.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) wrong labels (d) correct labels
11. 11.🔍 Which is the odd one out? (a) photos in many lights (b) copies of one photo (c) testing on training data (d) only one kind of example
12. 12.Which of these is good for training a model? (a) photos in many lights (b) very few examples (c) blurry, unclear photos (d) only one kind of example
13. 13.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
14. 14.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
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) spam (b) not spam
16. 16.True or false: “labels added at random” is bad for training a model. (a) False (b) True
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
18. 18.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
19. 19.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) checking labels twice (d) wrong labels
20. 20.🏷️ 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
21. 21.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
22. 22.True or false: “testing on training data” is bad for training a model. (a) False (b) True
23. 23.Which of these is bad for training a model? (a) clear, sharp examples (b) checking labels twice (c) testing on training data (d) correct labels
24. 24.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) clear, sharp examples (d) correct labels
25. 25.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) examples of every type (d) clear, sharp examples
26. 26.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
27. 27.🏷️ 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
28. 28.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
29. 29.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
30. 30.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) photos in many lights (d) many varied examples
31. 31.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
32. 32.🥭 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
33. 33.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) clear, sharp examples (d) testing on training data
34. 34.Which of these is good for training a model? (a) new data for testing (b) only one kind of example (c) copies of one photo (d) testing on training data
35. 35.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
36. 36.Which of these is bad for training a model? (a) clear, sharp examples (b) copies of one photo (c) examples of every type (d) new data for testing
37. 37.True or false: “very few examples” is bad for training a model. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) very few examples (d) many varied examples
39. 39.🏷️ 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
40. 40.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training

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