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Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 516 · 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.🏷️ 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.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) examples of every type (d) labels added at random
  3. 3.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
  4. 4.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
  5. 5.True or false: “checking labels twice” is good for training a model. (a) False (b) True
  6. 6.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) labels added at random (d) many varied examples
  7. 7.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it may get it wrong (d) it will turn yellow
  8. 8.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) many varied examples (d) only one kind of example
  9. 9.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
  10. 10.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
  11. 11.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) a sticker on a laptop (d) the right answer tag
  12. 12.🏷️ 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
  13. 13.True or false: “very few examples” is good for training a model. (a) False (b) True
  14. 14.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
  15. 15.🏷️ 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
  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 of these is bad for training a model? (a) correct labels (b) examples of every type (c) photos in many lights (d) wrong labels
  18. 18.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
  19. 19.🏷️ 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
  20. 20.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) a song about dogs (d) one single photo
  21. 21.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
  22. 22.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) many varied examples (d) only one kind of example
  23. 23.Which of these is good for training a model? (a) wrong labels (b) labels added at random (c) very few examples (d) photos in many lights
  24. 24.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) copies of one photo (d) blurry, unclear photos
  25. 25.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) correct labels (d) blurry, unclear photos
  26. 26.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
  27. 27.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
  28. 28.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
  29. 29.🏷️ 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
  30. 30.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
  31. 31.🔍 Which is the odd one out? (a) checking labels twice (b) wrong labels (c) many varied examples (d) photos in many lights
  32. 32.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
  33. 33.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) copies of one photo (d) checking labels twice
  34. 34.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) correct labels (d) copies of one photo
  35. 35.True or false: “new data for testing” is good for training a model. (a) False (b) True
  36. 36.True or false: “labels added at random” is bad for training a model. (a) False (b) True
  37. 37.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
  38. 38.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) many varied examples (d) testing on training data
  39. 39.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
  40. 40.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) very few examples (d) new data for testing

Answer key

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

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