Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 154 · 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 “spam”. (a) True (b) False
2. 2.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) wrong labels (d) correct labels
3. 3.🏷️ 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
4. 4.Which of these is good for training a model? (a) labels added at random (b) many varied examples (c) copies of one photo (d) very few examples
5. 5.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) copies of one photo (d) clear, sharp examples
6. 6.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) examples of every type (d) blurry, unclear photos
7. 7.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) one single photo (d) a song about dogs
9. 9.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) copies of one photo (d) new data for testing
10. 10.True or false: “many varied examples” is bad for training a model. (a) False (b) True
11. 11.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) checking labels twice (d) labels added at random
12. 12.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) photos in many lights (d) copies of one photo
13. 13.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
14. 14.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) new data for testing (d) only one kind of example
15. 15.🥭 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
16. 16.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
17. 17.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
18. 18.🏷️ 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
19. 19.True or false: “photos in many lights” is good for training a model. (a) False (b) True
20. 20.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
21. 21.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
22. 22.🏷️ 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
23. 23.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
24. 24.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
25. 25.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) correct labels (d) wrong labels
26. 26.True or false: “new data for testing” is good for training a model. (a) False (b) True
27. 27.Which of these is good for training a model? (a) wrong labels (b) examples of every type (c) only one kind of example (d) labels added at random
28. 28.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) photos in many lights (d) very few examples
29. 29.🔍 Which is the odd one out? (a) blurry, unclear photos (b) checking labels twice (c) wrong labels (d) copies of one photo
30. 30.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) many varied examples (d) wrong labels
31. 31.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
32. 32.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
33. 33.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) photos in many lights (d) wrong labels
34. 34.🏷️ 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
35. 35.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) testing on training data (d) correct labels
37. 37.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) blurry, unclear photos (d) testing on training data
39. 39.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
40. 40.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training

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