Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 453 · 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 “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
2. 2.🔍 Which is the odd one out? (a) clear, sharp examples (b) photos in many lights (c) wrong labels (d) examples of every type
3. 3.🏷️ 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) spam (b) not spam
4. 4.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) many varied examples (d) blurry, unclear photos
5. 5.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) very few examples (d) checking labels twice
6. 6.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
7. 7.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
8. 8.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) wrong labels (d) new data for testing
9. 9.🏷️ 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
10. 10.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
11. 11.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
12. 12.Which of these is bad for training a model? (a) wrong labels (b) correct labels (c) many varied examples (d) checking labels twice
13. 13.🏷️ 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
14. 14.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
15. 15.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
16. 16.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) blurry, unclear photos (d) many varied examples
17. 17.🏷️ 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
18. 18.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
19. 19.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
20. 20.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
21. 21.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) only one kind of example (d) checking labels twice
22. 22.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
23. 23.🏷️ 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
24. 24.Which of these is good for training a model? (a) copies of one photo (b) wrong labels (c) photos in many lights (d) only one kind of example
25. 25.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) wrong labels (d) labels added at random
26. 26.Which of these is good for training a model? (a) very few examples (b) checking labels twice (c) wrong labels (d) testing on training data
27. 27.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
28. 28.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
29. 29.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
30. 30.True or false: “examples of every type” is good for training a model. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) checking labels twice (d) testing on training data
32. 32.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
33. 33.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) only one kind of example (d) testing on training data
34. 34.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
35. 35.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
36. 36.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
37. 37.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
38. 38.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) correct labels (d) copies of one photo
39. 39.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
40. 40.🥭 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

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