Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 62 · 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) wrong labels (b) labels added at random (c) only one kind of example (d) examples of every type
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 is the odd one out? (a) very few examples (b) new data for testing (c) labels added at random (d) copies of one photo
4. 4.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
5. 5.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) new data for testing (d) wrong labels
6. 6.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
7. 7.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) testing on training data (d) clear, sharp examples
8. 8.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) many varied examples (d) labels added at random
9. 9.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
10. 10.🏷️ 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
11. 11.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) photos in many lights (d) copies of one photo
12. 12.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) clear, sharp examples (d) blurry, unclear photos
13. 13.True or false: “photos in many lights” is good for training a model. (a) True (b) False
14. 14.🏷️ 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
15. 15.True or false: “examples of every type” is good for training a model. (a) True (b) False
16. 16.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
17. 17.True or false: “very few examples” is good for training a model. (a) False (b) True
18. 18.🏷️ 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
19. 19.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) blurry, unclear photos (d) examples of every type
20. 20.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
21. 21.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
22. 22.True or false: “many varied examples” is good for training a model. (a) False (b) True
23. 23.🏷️ 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
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
25. 25.Which of these is bad for training a model? (a) labels added at random (b) photos in many lights (c) examples of every type (d) many varied examples
26. 26.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) slower to switch on (d) change colour
27. 27.🔍 Which is the odd one out? (a) very few examples (b) examples of every type (c) photos in many lights (d) many varied examples
28. 28.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
29. 29.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
30. 30.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
31. 31.True or false: “wrong labels” is good for training a model. (a) False (b) True
32. 32.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
33. 33.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
35. 35.Which of these is bad for training a model? (a) checking labels twice (b) blurry, unclear photos (c) clear, sharp examples (d) correct labels
36. 36.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) the right answer tag (d) a sticker on a laptop
37. 37.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
38. 38.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
39. 39.True or false: “many varied examples” is bad for training a model. (a) False (b) True
40. 40.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) blurry, unclear photos (d) photos in many lights

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