Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 209 · 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.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
2. 2.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
3. 3.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) correct labels (d) copies of one photo
4. 4.🏷️ 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
5. 5.True or false: “labels added at random” is bad for training a model. (a) False (b) True
6. 6.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
7. 7.🏷️ 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
8. 8.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) many varied examples (d) very few examples
9. 9.🏷️ 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
10. 10.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) wrong labels (d) clear, sharp examples
11. 11.Which of these is bad for training a model? (a) testing on training data (b) new data for testing (c) photos in many lights (d) checking labels twice
12. 12.🔍 Which is the odd one out? (a) only one kind of example (b) examples of every type (c) blurry, unclear photos (d) testing on training data
13. 13.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
14. 14.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
15. 15.🔍 Which is the odd one out? (a) correct labels (b) examples of every type (c) very few examples (d) photos in many lights
16. 16.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) a sticker on a laptop (d) the right answer tag
17. 17.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) clean screens (d) print pages
18. 18.True or false: “examples of every type” is good for training a model. (a) True (b) False
19. 19.Which of these is good for training a model? (a) copies of one photo (b) wrong labels (c) very few examples (d) correct labels
20. 20.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
21. 21.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) new data for testing (d) blurry, unclear photos
22. 22.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
23. 23.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) only one kind of example (d) many varied examples
24. 24.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
25. 25.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
26. 26.Which of these is bad for training a model? (a) examples of every type (b) many varied examples (c) very few examples (d) clear, sharp examples
27. 27.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) many labelled photos (d) one single photo
28. 28.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) testing on training data (d) labels added at random
29. 29.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
31. 31.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) examples of every type (d) copies of one photo
32. 32.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) new data for testing (d) wrong labels
33. 33.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
34. 34.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
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.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) wrong labels (d) correct labels
37. 37.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
38. 38.🏷️ 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
39. 39.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
40. 40.True or false: “copies of one photo” is good for training a model. (a) False (b) True

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