Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 63 · 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 good for training a model? (a) testing on training data (b) wrong labels (c) examples of every type (d) very few examples
2. 2.True or false: “many varied examples” is bad for training a model. (a) True (b) False
3. 3.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
4. 4.True or false: “testing on training data” is bad for training a model. (a) False (b) True
5. 5.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
6. 6.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
7. 7.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
8. 8.True or false: “correct labels” is good for training a model. (a) False (b) True
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) not spam (b) spam
10. 10.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
11. 11.True or false: “copies of one photo” is good for training a model. (a) True (b) False
12. 12.🏷️ 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
13. 13.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it will turn yellow (d) it may get it wrong
14. 14.🏷️ What is a “label” in machine learning? (a) the font size (b) the right answer tag (c) a sticker on a laptop (d) the price
15. 15.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
16. 16.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) blurry, unclear photos (d) new data for testing
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.🔍 Which is the odd one out? (a) blurry, unclear photos (b) clear, sharp examples (c) copies of one photo (d) only one kind of example
19. 19.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
20. 20.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) checking labels twice (d) very few examples
21. 21.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
22. 22.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
23. 23.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) wrong labels (d) correct labels
24. 24.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) forget everything (d) slower to switch on
25. 25.🐶 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) a song about dogs (d) many labelled photos
26. 26.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) wrong labels (d) clear, sharp examples
28. 28.🏷️ 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
29. 29.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
30. 30.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) many varied examples (d) correct labels
31. 31.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
32. 32.🏷️ 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
33. 33.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
34. 34.True or false: “examples of every type” is bad for training a model. (a) True (b) False
35. 35.Which of these is good for training a model? (a) testing on training data (b) checking labels twice (c) only one kind of example (d) labels added at random
36. 36.True or false: “new data for testing” is good for training a model. (a) False (b) True
37. 37.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
38. 38.Which of these is good for training a model? (a) photos in many lights (b) testing on training data (c) labels added at random (d) copies of one photo
39. 39.True or false: “examples of every type” is good for training a model. (a) False (b) True
40. 40.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) only one kind of example (d) very few examples

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