Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 255 · 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) examples of every type (c) labels added at random (d) photos in many lights
2. 2.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) testing on training data (d) new data for testing
3. 3.True or false: “many varied examples” is good for training a model. (a) True (b) False
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 “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
6. 6.🏷️ 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
7. 7.🏷️ 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
8. 8.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
9. 9.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
10. 10.🏷️ 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
11. 11.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) clear, sharp examples (d) only one kind of example
12. 12.True or false: “wrong labels” is bad for training a model. (a) False (b) True
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
14. 14.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) print pages (d) clean screens
15. 15.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
16. 16.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
17. 17.🔍 Which is the odd one out? (a) checking labels twice (b) very few examples (c) examples of every type (d) photos in many lights
18. 18.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
19. 19.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) correct labels (d) wrong labels
20. 20.True or false: “many varied examples” is bad for training a model. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) correct labels (d) photos in many lights
22. 22.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) wrong labels (d) clear, sharp examples
23. 23.🏷️ 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
24. 24.True or false: “checking labels twice” is good for training a model. (a) True (b) False
25. 25.True or false: “examples of every type” is bad for training a model. (a) True (b) False
26. 26.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
27. 27.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
28. 28.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
29. 29.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
30. 30.Which of these is bad for training a model? (a) examples of every type (b) many varied examples (c) new data for testing (d) blurry, unclear photos
31. 31.🏷️ 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
32. 32.Which of these is bad for training a model? (a) examples of every type (b) only one kind of example (c) checking labels twice (d) many varied examples
33. 33.Which of these is good for training a model? (a) wrong labels (b) new data for testing (c) testing on training data (d) blurry, unclear photos
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
35. 35.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
36. 36.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
37. 37.🏷️ 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
38. 38.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
39. 39.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
40. 40.True or false: “labels added at random” is bad for training a model. (a) True (b) False

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