Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 587 · 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.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
2. 2.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) checking labels twice (d) copies of one photo
3. 3.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) examples of every type (d) new data for testing
4. 4.🏷️ 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
5. 5.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) labels added at random (d) new data for testing
6. 6.True or false: “labels added at random” is good for training a model. (a) False (b) True
7. 7.🏷️ 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
8. 8.🔍 Which is the odd one out? (a) clear, sharp examples (b) examples of every type (c) checking labels twice (d) testing on training data
9. 9.True or false: “many varied examples” is bad for training a model. (a) False (b) True
10. 10.Which of these is good for training a model? (a) many varied examples (b) very few examples (c) wrong labels (d) blurry, unclear photos
11. 11.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) checking labels twice (d) blurry, unclear photos
12. 12.True or false: “correct labels” is bad for training a model. (a) False (b) True
13. 13.Which of these is good for training a model? (a) blurry, unclear photos (b) photos in many lights (c) wrong labels (d) very few examples
14. 14.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
15. 15.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the price (d) the font size
16. 16.True or false: “testing on training data” is good for training a model. (a) False (b) True
17. 17.🔍 Which is the odd one out? (a) only one kind of example (b) correct labels (c) labels added at random (d) very few examples
18. 18.🏷️ 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
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
20. 20.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
21. 21.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
22. 22.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) a list of rules only (d) one single photo
23. 23.🔍 Which is the odd one out? (a) copies of one photo (b) new data for testing (c) examples of every type (d) many varied examples
24. 24.🏷️ 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
25. 25.🔍 Which is the odd one out? (a) new data for testing (b) labels added at random (c) copies of one photo (d) wrong labels
26. 26.Which of these is good for training a model? (a) very few examples (b) new data for testing (c) copies of one photo (d) wrong labels
27. 27.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
28. 28.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) labels added at random (d) new data for testing
29. 29.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
30. 30.True or false: “correct labels” is good for training a model. (a) False (b) True
31. 31.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) only one kind of example (d) clear, sharp examples
32. 32.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) examples of every type (d) testing on training data
33. 33.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
34. 34.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
35. 35.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will be perfect (d) it will shut down
36. 36.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) correct labels (d) many varied examples
37. 37.🔍 Which is the odd one out? (a) blurry, unclear photos (b) checking labels twice (c) examples of every type (d) many varied examples
38. 38.True or false: “examples of every type” is good for training a model. (a) False (b) True
39. 39.True or false: “very few examples” is bad for training a model. (a) True (b) False
40. 40.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training

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