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Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 14 · 40 questions
TalentJR

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.

NameDateTime takenScore ___ / 40
  1. 1.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
  2. 2.True or false: “wrong labels” is good for training a model. (a) True (b) False
  3. 3.Which of these is good for training a model? (a) wrong labels (b) labels added at random (c) correct labels (d) very few examples
  4. 4.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
  5. 5.True or false: “correct labels” is bad for training a model. (a) True (b) False
  6. 6.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
  7. 7.Which of these is bad for training a model? (a) checking labels twice (b) new data for testing (c) labels added at random (d) examples of every type
  8. 8.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) clear, sharp examples (d) very few examples
  9. 9.Which of these is bad for training a model? (a) clear, sharp examples (b) wrong labels (c) examples of every type (d) correct labels
  10. 10.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
  11. 11.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
  12. 12.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
  13. 13.🏷️ 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
  14. 14.Which of these is good for training a model? (a) only one kind of example (b) labels added at random (c) examples of every type (d) very few examples
  15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
  16. 16.🏷️ 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
  17. 17.🔍 Which is the odd one out? (a) checking labels twice (b) testing on training data (c) wrong labels (d) labels added at random
  18. 18.🔍 Which is the odd one out? (a) correct labels (b) labels added at random (c) many varied examples (d) photos in many lights
  19. 19.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
  20. 20.🥭 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 may get it wrong (d) it will turn yellow
  21. 21.Which of these is good for training a model? (a) many varied examples (b) wrong labels (c) labels added at random (d) testing on training data
  22. 22.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
  23. 23.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) labels added at random (d) correct labels
  24. 24.True or false: “many varied examples” is good for training a model. (a) True (b) False
  25. 25.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
  26. 26.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
  27. 27.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) many labelled photos (d) one single photo
  28. 28.True or false: “labels added at random” is good for training a model. (a) False (b) True
  29. 29.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
  30. 30.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
  31. 31.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
  32. 32.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
  33. 33.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
  34. 34.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
  35. 35.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
  36. 36.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) many varied examples (d) examples of every type
  37. 37.🔍 Which is the odd one out? (a) labels added at random (b) testing on training data (c) clear, sharp examples (d) only one kind of example
  38. 38.🏷️ 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
  39. 39.🏷️ 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
  40. 40.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False

Answer key

  1. the right answer tag
  2. False
  3. correct labels
  4. learn from data
  5. False
  6. bad for training
  7. labels added at random
  8. clear, sharp examples
  9. wrong labels
  10. better at its task
  11. not spam
  12. False
  13. spam
  14. examples of every type
  15. spam
  16. spam
  17. checking labels twice
  18. labels added at random
  19. True
  20. it may get it wrong
  21. many varied examples
  22. False
  23. correct labels
  24. True
  25. bad for training
  26. good for training
  27. many labelled photos
  28. False
  29. False
  30. good for training
  31. True
  32. good for training
  33. good for training
  34. good for training
  35. True
  36. copies of one photo
  37. clear, sharp examples
  38. spam
  39. spam
  40. False

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