Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 571 · 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? “Happy birthday! See you at lunch” (a) not spam (b) spam
2. 2.True or false: “examples of every type” is good for training a model. (a) False (b) True
3. 3.🏷️ 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
4. 4.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
5. 5.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) very few examples (d) only one kind of example
6. 6.True or false: “testing on training data” is bad for training a model. (a) False (b) True
7. 7.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) blurry, unclear photos (d) new data for testing
8. 8.🏷️ 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
9. 9.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
10. 10.Which of these is bad for training a model? (a) very few examples (b) examples of every type (c) clear, sharp examples (d) correct labels
11. 11.🏷️ 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) not spam (b) spam
12. 12.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
13. 13.Which of these is good for training a model? (a) only one kind of example (b) clear, sharp examples (c) wrong labels (d) labels added at random
14. 14.🏷️ 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
15. 15.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the font size (d) the right answer tag
16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
17. 17.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) examples of every type (d) very few examples
18. 18.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) checking labels twice (d) wrong labels
19. 19.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) correct labels (d) blurry, unclear photos
20. 20.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
21. 21.🏷️ 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
22. 22.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
23. 23.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
24. 24.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
25. 25.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
26. 26.🔍 Which is the odd one out? (a) clear, sharp examples (b) many varied examples (c) examples of every type (d) only one kind of example
27. 27.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it will be perfect (d) it may get it wrong
28. 28.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
29. 29.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
30. 30.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
31. 31.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
32. 32.True or false: “wrong labels” is bad for training a model. (a) True (b) False
33. 33.Which of these is bad for training a model? (a) only one kind of example (b) clear, sharp examples (c) photos in many lights (d) new data for testing
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
35. 35.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
36. 36.🔍 Which is the odd one out? (a) clear, sharp examples (b) testing on training data (c) very few examples (d) only one kind of example
37. 37.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
39. 39.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
40. 40.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training

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