Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 525 · 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 good for training a model? (a) testing on training data (b) blurry, unclear photos (c) correct labels (d) very few examples
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
3. 3.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
4. 4.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
5. 5.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
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 “not spam”. (a) False (b) True
8. 8.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
9. 9.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) new data for testing (d) very few examples
10. 10.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) correct labels (d) testing on training data
11. 11.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the right answer tag (d) the font size
12. 12.Which of these is bad for training a model? (a) new data for testing (b) wrong labels (c) many varied examples (d) clear, sharp examples
13. 13.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) only one kind of example (d) correct labels
14. 14.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) examples of every type (d) labels added at random
15. 15.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) one single photo (d) a list of rules only
16. 16.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
17. 17.🔍 Which is the odd one out? (a) photos in many lights (b) only one kind of example (c) labels added at random (d) wrong labels
18. 18.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
19. 19.🔍 Which is the odd one out? (a) copies of one photo (b) new data for testing (c) wrong labels (d) only one kind of example
20. 20.Which of these is good for training a model? (a) wrong labels (b) copies of one photo (c) clear, sharp examples (d) labels added at random
21. 21.True or false: “wrong labels” is bad for training a model. (a) True (b) False
22. 22.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
23. 23.🏷️ 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
24. 24.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) copies of one photo (d) photos in many lights
25. 25.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
26. 26.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
27. 27.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) labels added at random (d) checking labels twice
28. 28.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) blurry, unclear photos (d) photos in many lights
29. 29.🔍 Which is the odd one out? (a) labels added at random (b) testing on training data (c) copies of one photo (d) clear, sharp examples
30. 30.True or false: “examples of every type” is bad for training a model. (a) True (b) False
31. 31.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
32. 32.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
33. 33.🏷️ 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
34. 34.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
35. 35.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
36. 36.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
37. 37.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
38. 38.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) only one kind of example (d) new data for testing
39. 39.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
40. 40.True or false: “very few examples” is bad for training a model. (a) False (b) True

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