Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 593 · 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.🏷️ 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
2. 2.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
3. 3.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
4. 4.Which of these is good for training a model? (a) new data for testing (b) testing on training data (c) very few examples (d) only one kind of example
5. 5.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
6. 6.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
7. 7.🏷️ 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
8. 8.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
10. 10.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
11. 11.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
12. 12.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) very few examples (d) correct labels
13. 13.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
14. 14.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
15. 15.🔍 Which is the odd one out? (a) many varied examples (b) labels added at random (c) wrong labels (d) copies of one photo
16. 16.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
17. 17.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
19. 19.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
20. 20.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) copies of one photo (d) checking labels twice
21. 21.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
22. 22.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
23. 23.🏷️ 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
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.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) very few examples (d) blurry, unclear photos
26. 26.🏷️ 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
27. 27.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) clear, sharp examples (d) labels added at random
28. 28.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) clear, sharp examples (d) very few examples
29. 29.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
30. 30.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
31. 31.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
32. 32.True or false: “correct labels” is bad for training a model. (a) True (b) False
33. 33.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) very few examples (d) examples of every type
34. 34.Which of these is bad for training a model? (a) testing on training data (b) clear, sharp examples (c) correct labels (d) examples of every type
35. 35.True or false: “examples of every type” is bad for training a model. (a) True (b) False
36. 36.True or false: “many varied examples” is bad for training a model. (a) False (b) True
37. 37.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
38. 38.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
39. 39.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
40. 40.True or false: “checking labels twice” is bad for training a model. (a) True (b) False

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