Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 313 · 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 “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
2. 2.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
3. 3.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
4. 4.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
5. 5.🔍 Which is the odd one out? (a) wrong labels (b) testing on training data (c) blurry, unclear photos (d) new data for testing
6. 6.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
7. 7.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) many labelled photos (d) a song about dogs
9. 9.Which of these is bad for training a model? (a) clear, sharp examples (b) checking labels twice (c) many varied examples (d) wrong labels
10. 10.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
11. 11.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
12. 12.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
13. 13.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) the price (d) a sticker on a laptop
14. 14.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
15. 15.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
16. 16.🏷️ 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
17. 17.🏷️ 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
18. 18.🔍 Which is the odd one out? (a) clear, sharp examples (b) only one kind of example (c) labels added at random (d) very few examples
19. 19.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
20. 20.True or false: “very few examples” is good for training a model. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) only one kind of example (b) wrong labels (c) blurry, unclear photos (d) examples of every type
22. 22.🔍 Which is the odd one out? (a) checking labels twice (b) labels added at random (c) copies of one photo (d) very few examples
23. 23.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
24. 24.🏷️ 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
25. 25.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
26. 26.Which of these is bad for training a model? (a) examples of every type (b) photos in many lights (c) new data for testing (d) very few examples
27. 27.Which of these is good for training a model? (a) wrong labels (b) labels added at random (c) blurry, unclear photos (d) photos in many lights
28. 28.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
29. 29.📚 Good or bad for training a model: photos in many lights? (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 shut down (b) it may get it wrong (c) it will be perfect (d) it will turn yellow
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.True or false: “testing on training data” is bad for training a model. (a) True (b) False
33. 33.🏷️ 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
34. 34.Which of these is good for training a model? (a) clear, sharp examples (b) very few examples (c) wrong labels (d) only one kind of example
35. 35.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
36. 36.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
37. 37.🏷️ 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
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
39. 39.Which of these is good for training a model? (a) examples of every type (b) wrong labels (c) very few examples (d) testing on training data
40. 40.🏷️ 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

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