Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 211 · 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.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
2. 2.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
3. 3.🏷️ 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
4. 4.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
5. 5.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) examples of every type (d) correct labels
7. 7.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) a list of rules only (d) one single photo
8. 8.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
9. 9.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) photos in many lights (d) wrong labels
10. 10.🏷️ 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
11. 11.Which of these is bad for training a model? (a) photos in many lights (b) blurry, unclear photos (c) many varied examples (d) clear, sharp examples
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
13. 13.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
14. 14.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
15. 15.🏷️ 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
16. 16.Which of these is bad for training a model? (a) photos in many lights (b) checking labels twice (c) testing on training data (d) many varied examples
17. 17.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
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? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
20. 20.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) clean screens (d) print pages
21. 21.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
22. 22.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) very few examples (d) photos in many lights
23. 23.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
24. 24.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it may get it wrong (d) it will be perfect
25. 25.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
26. 26.Which of these is good for training a model? (a) checking labels twice (b) testing on training data (c) copies of one photo (d) blurry, unclear photos
27. 27.True or false: “correct labels” is good for training a model. (a) False (b) True
28. 28.True or false: “testing on training data” is good for training a model. (a) False (b) True
29. 29.🏷️ 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
30. 30.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) the right answer tag (d) a sticker on a laptop
31. 31.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
32. 32.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) labels added at random (d) photos in many lights
33. 33.Which of these is bad for training a model? (a) wrong labels (b) examples of every type (c) correct labels (d) many varied examples
34. 34.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
35. 35.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
36. 36.Which of these is bad for training a model? (a) examples of every type (b) labels added at random (c) checking labels twice (d) new data for testing
37. 37.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
38. 38.Which of these is good for training a model? (a) only one kind of example (b) copies of one photo (c) blurry, unclear photos (d) clear, sharp examples
39. 39.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
40. 40.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True

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