Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 32 · 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 bad for training a model? (a) very few examples (b) clear, sharp examples (c) photos in many lights (d) correct labels
2. 2.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
3. 3.🏷️ 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
4. 4.Which of these is good for training a model? (a) blurry, unclear photos (b) labels added at random (c) wrong labels (d) new data for testing
5. 5.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) copies of one photo (d) many varied examples
6. 6.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) copies of one photo (d) many varied examples
7. 7.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) better at its task (d) forget everything
8. 8.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
9. 9.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) checking labels twice (d) blurry, unclear photos
10. 10.Which of these is bad for training a model? (a) many varied examples (b) clear, sharp examples (c) labels added at random (d) photos in many lights
11. 11.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) many labelled photos (d) one single photo
12. 12.True or false: “correct labels” is bad for training a model. (a) True (b) False
13. 13.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) a sticker on a laptop (d) the font size
14. 14.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) checking labels twice (d) copies of one photo
15. 15.True or false: “examples of every type” is bad for training a model. (a) False (b) True
16. 16.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) checking labels twice (d) very few examples
17. 17.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
18. 18.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) copies of one photo (b) labels added at random (c) testing on training data (d) new data for testing
20. 20.🏷️ 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
21. 21.True or false: “many varied examples” is good for training a model. (a) True (b) False
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) not spam (b) spam
23. 23.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) wrong labels (d) labels added at random
24. 24.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
25. 25.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) only one kind of example (d) examples of every type
26. 26.🏷️ 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
27. 27.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
28. 28.True or false: “correct labels” is good for training a model. (a) True (b) False
29. 29.🥭 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
30. 30.🏷️ 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
31. 31.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) correct labels (b) only one kind of example (c) clear, sharp examples (d) new data for testing
33. 33.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
34. 34.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
35. 35.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
36. 36.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
37. 37.🏷️ 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
38. 38.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) copies of one photo (d) checking labels twice
39. 39.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
40. 40.Which of these is good for training a model? (a) many varied examples (b) labels added at random (c) copies of one photo (d) wrong labels

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