Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 199 · 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.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
3. 3.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) many labelled photos (d) a list of rules only
4. 4.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
5. 5.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
6. 6.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
7. 7.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
8. 8.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
9. 9.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) testing on training data (d) correct labels
10. 10.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
11. 11.True or false: “correct labels” is bad for training a model. (a) False (b) True
12. 12.Which of these is good for training a model? (a) copies of one photo (b) wrong labels (c) clear, sharp examples (d) labels added at random
13. 13.Which of these is good for training a model? (a) examples of every type (b) very few examples (c) only one kind of example (d) copies of one photo
14. 14.🔍 Which is the odd one out? (a) clear, sharp examples (b) correct labels (c) photos in many lights (d) labels added at random
15. 15.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) examples of every type (d) correct labels
16. 16.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
17. 17.🏷️ 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
18. 18.🏷️ 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
19. 19.True or false: “checking labels twice” is good for training a model. (a) True (b) False
20. 20.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) wrong labels (d) only one kind of example
21. 21.True or false: “correct labels” is good for training a model. (a) False (b) True
22. 22.True or false: “examples of every type” is good for training a model. (a) True (b) False
23. 23.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
24. 24.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) copies of one photo (d) labels added at random
25. 25.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
26. 26.Which of these is good for training a model? (a) new data for testing (b) only one kind of example (c) labels added at random (d) blurry, unclear photos
27. 27.True or false: “very few examples” is bad for training a model. (a) False (b) True
28. 28.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
29. 29.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it may get it wrong (d) it will turn yellow
30. 30.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) correct labels (d) wrong labels
31. 31.🏷️ 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
32. 32.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
33. 33.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
34. 34.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
35. 35.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) only one kind of example (d) many varied examples
36. 36.Which of these is bad for training a model? (a) new data for testing (b) labels added at random (c) many varied examples (d) correct labels
37. 37.Which of these is bad for training a model? (a) clear, sharp examples (b) testing on training data (c) photos in many lights (d) examples of every type
38. 38.True or false: “very few examples” is good for training a model. (a) False (b) True
39. 39.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
40. 40.Which of these is good for training a model? (a) labels added at random (b) copies of one photo (c) very few examples (d) photos in many lights

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