Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 293 · 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) clear, sharp examples (b) wrong labels (c) photos in many lights (d) examples of every type
2. 2.True or false: “many varied examples” is bad for training a model. (a) True (b) False
3. 3.🔍 Which is the odd one out? (a) wrong labels (b) labels added at random (c) testing on training data (d) new data for testing
4. 4.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
5. 5.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
6. 6.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) many varied examples (d) photos in many lights
8. 8.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) many labelled photos (d) a list of rules only
10. 10.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
11. 11.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) forget everything (d) slower to switch on
12. 12.Which of these is bad for training a model? (a) new data for testing (b) checking labels twice (c) testing on training data (d) photos in many lights
13. 13.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
14. 14.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) testing on training data (d) only one kind of example
15. 15.🏷️ 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
16. 16.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
17. 17.🔍 Which is the odd one out? (a) correct labels (b) labels added at random (c) photos in many lights (d) new data for testing
18. 18.Which of these is good for training a model? (a) labels added at random (b) photos in many lights (c) very few examples (d) testing on training data
19. 19.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) blurry, unclear photos (d) checking labels twice
20. 20.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
21. 21.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
22. 22.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) testing on training data (d) correct labels
23. 23.Which of these is bad for training a model? (a) clear, sharp examples (b) examples of every type (c) new data for testing (d) wrong labels
24. 24.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
25. 25.🔍 Which is the odd one out? (a) correct labels (b) copies of one photo (c) clear, sharp examples (d) checking labels twice
26. 26.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) charge faster (d) print pages
27. 27.🔍 Which is the odd one out? (a) only one kind of example (b) very few examples (c) photos in many lights (d) testing on training data
28. 28.🏷️ 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
29. 29.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) very few examples (d) clear, sharp examples
30. 30.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) only one kind of example (d) examples of every type
31. 31.Which of these is good for training a model? (a) labels added at random (b) copies of one photo (c) wrong labels (d) checking labels twice
32. 32.True or false: “wrong labels” is bad for training a model. (a) False (b) True
33. 33.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
34. 34.🏷️ 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
35. 35.🏷️ 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
36. 36.Which of these is good for training a model? (a) very few examples (b) new data for testing (c) copies of one photo (d) wrong labels
37. 37.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) clear, sharp examples (d) testing on training data
38. 38.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) very few examples (d) photos in many lights
39. 39.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
40. 40.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True

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