Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 230 · 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 is the odd one out? (a) many varied examples (b) checking labels twice (c) correct labels (d) wrong labels
2. 2.🏷️ 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
3. 3.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) many labelled photos (d) a list of rules only
5. 5.🔍 Which is the odd one out? (a) checking labels twice (b) labels added at random (c) very few examples (d) wrong labels
6. 6.🏷️ 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
7. 7.True or false: “wrong labels” is good for training a model. (a) False (b) True
8. 8.True or false: “photos in many lights” is good for training a model. (a) False (b) True
9. 9.🥭 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
10. 10.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
11. 11.Which of these is bad for training a model? (a) very few examples (b) many varied examples (c) examples of every type (d) correct labels
12. 12.🏷️ 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
13. 13.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
14. 14.True or false: “many varied examples” is good for training a model. (a) True (b) False
15. 15.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
16. 16.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) labels added at random (d) very few examples
17. 17.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
18. 18.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) photos in many lights (d) copies of one photo
19. 19.🏷️ 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
20. 20.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
21. 21.Which of these is bad for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) examples of every type (d) many varied examples
22. 22.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on
23. 23.True or false: “many varied examples” is bad for training a model. (a) True (b) False
24. 24.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
25. 25.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
26. 26.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the price (d) the right answer tag
27. 27.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) correct labels (d) labels added at random
28. 28.Which of these is good for training a model? (a) copies of one photo (b) correct labels (c) only one kind of example (d) testing on training data
29. 29.🔍 Which is the odd one out? (a) correct labels (b) copies of one photo (c) checking labels twice (d) many varied examples
30. 30.True or false: “examples of every type” is bad for training a model. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) photos in many lights (d) blurry, unclear photos
32. 32.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
33. 33.🏷️ 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
34. 34.Which of these is bad for training a model? (a) photos in many lights (b) labels added at random (c) clear, sharp examples (d) new data for testing
35. 35.🏷️ 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
36. 36.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
37. 37.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
38. 38.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) correct labels (d) new data for testing
39. 39.True or false: “very few examples” is good for training a model. (a) True (b) False
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam

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