Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 544 · 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 bad for training a model? (a) clear, sharp examples (b) correct labels (c) labels added at random (d) new data for testing
2. 2.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
3. 3.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
4. 4.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
5. 5.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) labels added at random (d) correct labels
6. 6.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
7. 7.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
8. 8.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) checking labels twice (d) correct labels
9. 9.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
10. 10.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
11. 11.Which of these is bad for training a model? (a) clear, sharp examples (b) only one kind of example (c) photos in many lights (d) many varied examples
12. 12.🔍 Which is the odd one out? (a) testing on training data (b) labels added at random (c) correct labels (d) blurry, unclear photos
13. 13.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
14. 14.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) photos in many lights (d) very few examples
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
16. 16.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
18. 18.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
19. 19.Which of these is good for training a model? (a) testing on training data (b) examples of every type (c) blurry, unclear photos (d) only one kind of example
20. 20.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
21. 21.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) many varied examples (d) labels added at random
22. 22.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
23. 23.Which of these is good for training a model? (a) clear, sharp examples (b) testing on training data (c) very few examples (d) labels added at random
24. 24.🏷️ 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
25. 25.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
26. 26.True or false: “examples of every type” is bad for training a model. (a) True (b) False
27. 27.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) new data for testing (d) labels added at random
28. 28.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
30. 30.🔍 Which is the odd one out? (a) new data for testing (b) very few examples (c) examples of every type (d) photos in many lights
31. 31.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
32. 32.🏷️ 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
33. 33.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
34. 34.🔍 Which is the odd one out? (a) blurry, unclear photos (b) wrong labels (c) labels added at random (d) clear, sharp examples
35. 35.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) slower to switch on (d) forget everything
36. 36.🏷️ 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
37. 37.True or false: “labels added at random” is bad for training a model. (a) True (b) False
38. 38.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
39. 39.🏷️ 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
40. 40.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training

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