Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 331 · 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 of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) new data for testing (d) labels added at random
2. 2.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
3. 3.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) spam (b) not spam
4. 4.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
5. 5.🔍 Which is the odd one out? (a) wrong labels (b) copies of one photo (c) checking labels twice (d) testing on training data
6. 6.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) labels added at random (b) examples of every type (c) wrong labels (d) only one kind of example
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
9. 9.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
10. 10.🔍 Which is the odd one out? (a) only one kind of example (b) checking labels twice (c) many varied examples (d) clear, sharp examples
11. 11.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
12. 12.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will be perfect (c) it will shut down (d) it may get it wrong
13. 13.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
14. 14.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
15. 15.True or false: “correct labels” is good for training a model. (a) False (b) True
16. 16.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) wrong labels (d) many varied examples
17. 17.🏷️ 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
18. 18.Which of these is bad for training a model? (a) many varied examples (b) only one kind of example (c) new data for testing (d) examples of every type
19. 19.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
20. 20.🔍 Which is the odd one out? (a) clear, sharp examples (b) many varied examples (c) labels added at random (d) new data for testing
21. 21.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
22. 22.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
23. 23.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
24. 24.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) wrong labels (d) new data for testing
25. 25.🏷️ 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
26. 26.Which of these is good for training a model? (a) blurry, unclear photos (b) many varied examples (c) wrong labels (d) testing on training data
27. 27.Which of these is bad for training a model? (a) testing on training data (b) many varied examples (c) checking labels twice (d) correct labels
28. 28.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) wrong labels (d) copies of one photo
29. 29.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) clear, sharp examples (d) checking labels twice
30. 30.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) copies of one photo (d) examples of every type
31. 31.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
32. 32.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
33. 33.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
34. 34.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
35. 35.🐶 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
36. 36.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
37. 37.🏷️ 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
38. 38.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) correct labels (d) only one kind of example
39. 39.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
40. 40.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training

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