Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 290 · 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.True or false: “labels added at random” is bad for training a model. (a) True (b) False
2. 2.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a song about dogs (d) a list of rules only
3. 3.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) wrong labels (d) checking labels twice
4. 4.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
5. 5.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) photos in many lights (d) very few examples
6. 6.🏷️ 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
7. 7.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
8. 8.True or false: “many varied examples” is bad for training a model. (a) True (b) False
9. 9.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) examples of every type (d) blurry, unclear photos
10. 10.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
11. 11.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
12. 12.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
13. 13.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) photos in many lights (d) new data for testing
14. 14.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
15. 15.🔍 Which is the odd one out? (a) blurry, unclear photos (b) clear, sharp examples (c) correct labels (d) examples of every type
16. 16.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
17. 17.🔍 Which is the odd one out? (a) only one kind of example (b) many varied examples (c) photos in many lights (d) correct labels
18. 18.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) examples of every type (d) testing on training data
20. 20.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
21. 21.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
22. 22.🔍 Which is the odd one out? (a) correct labels (b) labels added at random (c) new data for testing (d) checking labels twice
23. 23.🔍 Which is the odd one out? (a) only one kind of example (b) very few examples (c) new data for testing (d) labels added at random
24. 24.True or false: “testing on training data” is good for training a model. (a) True (b) False
25. 25.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
26. 26.🏷️ 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
27. 27.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
28. 28.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
29. 29.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
30. 30.Which of these is bad for training a model? (a) copies of one photo (b) correct labels (c) many varied examples (d) photos in many lights
31. 31.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
32. 32.🏷️ 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
33. 33.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
34. 34.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
35. 35.🏷️ 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
36. 36.True or false: “labels added at random” is good for training a model. (a) True (b) False
37. 37.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
38. 38.Which of these is good for training a model? (a) blurry, unclear photos (b) many varied examples (c) wrong labels (d) copies of one photo
39. 39.True or false: “new data for testing” is bad for training a model. (a) True (b) False
40. 40.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training

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