Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 457 · 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) blurry, unclear photos (b) very few examples (c) many varied examples (d) copies of one photo
2. 2.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will turn yellow (d) it will shut down
3. 3.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) many varied examples (d) only one kind of example
4. 4.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
5. 5.Which of these is good for training a model? (a) correct labels (b) only one kind of example (c) blurry, unclear photos (d) wrong labels
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
7. 7.Which of these is bad for training a model? (a) correct labels (b) only one kind of example (c) checking labels twice (d) many varied examples
8. 8.🏷️ 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
9. 9.Which of these is bad for training a model? (a) wrong labels (b) examples of every type (c) new data for testing (d) clear, sharp examples
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
11. 11.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
12. 12.True or false: “examples of every type” is good for training a model. (a) False (b) True
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
14. 14.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
15. 15.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
17. 17.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
18. 18.Which of these is bad for training a model? (a) new data for testing (b) checking labels twice (c) testing on training data (d) many varied examples
19. 19.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) blurry, unclear photos (d) copies of one photo
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
21. 21.Which of these is good for training a model? (a) checking labels twice (b) wrong labels (c) testing on training data (d) very few examples
22. 22.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
23. 23.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) examples of every type (d) testing on training data
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
25. 25.🔍 Which is the odd one out? (a) checking labels twice (b) new data for testing (c) wrong labels (d) correct labels
26. 26.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
27. 27.True or false: “copies of one photo” is good for training a model. (a) True (b) False
28. 28.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) new data for testing (d) copies of one photo
30. 30.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
31. 31.🏷️ 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
32. 32.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) many varied examples (d) copies of one photo
33. 33.🔍 Which is the odd one out? (a) testing on training data (b) labels added at random (c) blurry, unclear photos (d) many varied examples
34. 34.🏷️ 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
35. 35.True or false: “only one kind of example” is good for training a model. (a) True (b) False
36. 36.🔍 Which is the odd one out? (a) testing on training data (b) checking labels twice (c) only one kind of example (d) blurry, unclear photos
37. 37.🏷️ 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
38. 38.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) photos in many lights (d) very few examples
39. 39.🏷️ 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
40. 40.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training

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