Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 88 · 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) copies of one photo (b) testing on training data (c) labels added at random (d) many varied examples
2. 2.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
3. 3.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
4. 4.🔍 Which is the odd one out? (a) only one kind of example (b) examples of every type (c) wrong labels (d) very few examples
5. 5.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
6. 6.📚 Usually, more good examples make a model… (a) better at its task (b) slower to switch on (c) forget everything (d) change colour
7. 7.Which of these is bad for training a model? (a) new data for testing (b) testing on training data (c) photos in many lights (d) checking labels twice
8. 8.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) photos in many lights (d) wrong labels
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
10. 10.🏷️ 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
11. 11.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
12. 12.🔍 Which is the odd one out? (a) labels added at random (b) wrong labels (c) copies of one photo (d) photos in many lights
13. 13.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
14. 14.📚 Good or bad for training a model: wrong labels? (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? “Share your OTP to get cashback” (a) not spam (b) spam
16. 16.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
17. 17.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) charge faster (d) clean screens
18. 18.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
19. 19.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) very few examples (d) examples of every type
20. 20.🔍 Which is the odd one out? (a) correct labels (b) testing on training data (c) blurry, unclear photos (d) copies of one photo
21. 21.True or false: “examples of every type” is bad for training a model. (a) False (b) True
22. 22.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
23. 23.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
24. 24.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
25. 25.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
26. 26.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
27. 27.🏷️ 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
28. 28.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) photos in many lights (b) examples of every type (c) testing on training data (d) new data for testing
30. 30.🏷️ 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
31. 31.True or false: “many varied examples” is good for training a model. (a) False (b) True
32. 32.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) very few examples (d) testing on training data
33. 33.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will turn yellow (c) it will shut down (d) it may get it wrong
34. 34.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) correct labels (d) blurry, unclear photos
35. 35.Which of these is good for training a model? (a) clear, sharp examples (b) only one kind of example (c) testing on training data (d) wrong labels
36. 36.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
37. 37.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) copies of one photo (d) new data for testing
38. 38.🔍 Which is the odd one out? (a) correct labels (b) only one kind of example (c) many varied examples (d) new data for testing
39. 39.🏷️ 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
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam

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