Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 408 · 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 is the odd one out? (a) examples of every type (b) copies of one photo (c) many varied examples (d) new data for testing
2. 2.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
3. 3.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) many varied examples (d) only one kind of example
4. 4.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) wrong labels
5. 5.🏷️ 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
6. 6.True or false: “blurry, unclear photos” is bad 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? “Share your OTP to get cashback” (a) spam (b) not spam
8. 8.🏷️ 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
9. 9.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
10. 10.🔍 Which is the odd one out? (a) blurry, unclear photos (b) photos in many lights (c) very few examples (d) wrong labels
11. 11.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) testing on training data (d) examples of every type
12. 12.Which of these is bad for training a model? (a) clear, sharp examples (b) correct labels (c) new data for testing (d) only one kind of example
13. 13.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
14. 14.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
15. 15.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
16. 16.🏷️ 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
17. 17.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) testing on training data (d) many varied examples
18. 18.Which of these is good for training a model? (a) wrong labels (b) new data for testing (c) very few examples (d) testing on training data
19. 19.🏷️ 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
20. 20.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
21. 21.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
22. 22.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
23. 23.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) examples of every type (d) wrong labels
24. 24.🔍 Which is the odd one out? (a) copies of one photo (b) clear, sharp examples (c) blurry, unclear photos (d) only one kind of example
25. 25.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) blurry, unclear photos (d) many varied examples
26. 26.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
27. 27.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
28. 28.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
29. 29.True or false: “wrong labels” is bad for training a model. (a) False (b) True
30. 30.🔍 Which is the odd one out? (a) correct labels (b) only one kind of example (c) very few examples (d) testing on training data
31. 31.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
32. 32.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
33. 33.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
34. 34.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
35. 35.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
36. 36.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
37. 37.True or false: “new data for testing” is bad for training a model. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) only one kind of example (d) new data for testing
39. 39.🔍 Which is the odd one out? (a) wrong labels (b) checking labels twice (c) blurry, unclear photos (d) only one kind of example
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam

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