Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 371 · 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.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
2. 2.🏷️ 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
3. 3.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) examples of every type (d) wrong labels
4. 4.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) photos in many lights (d) checking labels twice
5. 5.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) checking labels twice (d) labels added at random
6. 6.True or false: “examples of every type” is good for training a model. (a) False (b) True
7. 7.Which of these is good for training a model? (a) only one kind of example (b) photos in many lights (c) labels added at random (d) wrong labels
8. 8.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
9. 9.🔍 Which is the odd one out? (a) wrong labels (b) examples of every type (c) very few examples (d) blurry, unclear photos
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
11. 11.True or false: “correct labels” is bad for training a model. (a) True (b) False
12. 12.True or false: “many varied examples” is bad for training a model. (a) True (b) False
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) many varied examples (c) correct labels (d) checking labels twice
14. 14.🔍 Which is the odd one out? (a) new data for testing (b) wrong labels (c) labels added at random (d) copies of one photo
15. 15.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
16. 16.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
17. 17.Which of these is good for training a model? (a) blurry, unclear photos (b) clear, sharp examples (c) very few examples (d) wrong labels
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
19. 19.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
20. 20.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) copies of one photo (d) correct labels
21. 21.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
22. 22.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
23. 23.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
24. 24.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) only one kind of example (d) checking labels twice
25. 25.True or false: “very few examples” is good for training a model. (a) False (b) True
26. 26.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
27. 27.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) correct labels (d) only one kind of example
28. 28.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
29. 29.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
30. 30.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
32. 32.True or false: “examples of every type” is bad for training a model. (a) True (b) False
33. 33.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) learn from data (d) print pages
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.Which of these is good for training a model? (a) new data for testing (b) blurry, unclear photos (c) very few examples (d) only one kind of example
36. 36.True or false: “labels added at random” is good for training a model. (a) True (b) False
37. 37.🔍 Which is the odd one out? (a) labels added at random (b) wrong labels (c) clear, sharp examples (d) only one kind of example
38. 38.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
39. 39.🔍 Which is the odd one out? (a) checking labels twice (b) very few examples (c) testing on training data (d) labels added at random
40. 40.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training

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