Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 181 · 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) correct labels (b) new data for testing (c) wrong labels (d) photos in many lights
2. 2.Which of these is bad for training a model? (a) checking labels twice (b) new data for testing (c) only one kind of example (d) clear, sharp examples
3. 3.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
4. 4.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
5. 5.🔍 Which is the odd one out? (a) photos in many lights (b) wrong labels (c) very few examples (d) blurry, unclear photos
6. 6.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) blurry, unclear photos (d) many varied examples
7. 7.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) charge faster (d) clean screens
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
9. 9.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
11. 11.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) testing on training data (d) correct labels
12. 12.True or false: “very few examples” is good for training a model. (a) True (b) False
13. 13.Which of these is bad for training a model? (a) examples of every type (b) checking labels twice (c) clear, sharp examples (d) blurry, unclear photos
14. 14.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
15. 15.🏷️ 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
16. 16.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
17. 17.🏷️ 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
18. 18.Which of these is good for training a model? (a) clear, sharp examples (b) labels added at random (c) only one kind of example (d) copies of one photo
19. 19.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
20. 20.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
21. 21.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) copies of one photo (d) new data for testing
22. 22.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it will be perfect (d) it may get it wrong
23. 23.True or false: “correct labels” is good for training a model. (a) False (b) True
24. 24.🏷️ 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
25. 25.🔍 Which is the odd one out? (a) blurry, unclear photos (b) examples of every type (c) labels added at random (d) only one kind of example
26. 26.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) forget everything (d) slower to switch on
27. 27.🏷️ 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
28. 28.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) new data for testing (d) labels added at random
30. 30.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
31. 31.True or false: “wrong labels” is good for training a model. (a) True (b) False
32. 32.Which of these is bad for training a model? (a) checking labels twice (b) photos in many lights (c) testing on training data (d) new data for testing
33. 33.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) very few examples (d) correct labels
34. 34.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
35. 35.Which of these is good for training a model? (a) checking labels twice (b) labels added at random (c) copies of one photo (d) testing on training data
36. 36.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
37. 37.🔍 Which is the odd one out? (a) blurry, unclear photos (b) new data for testing (c) only one kind of example (d) labels added at random
38. 38.🏷️ 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
39. 39.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
40. 40.🏷️ 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

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