Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 445 · 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.🏷️ 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
2. 2.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
3. 3.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
4. 4.🔍 Which is the odd one out? (a) only one kind of example (b) many varied examples (c) checking labels twice (d) correct labels
5. 5.🔍 Which is the odd one out? (a) labels added at random (b) photos in many lights (c) testing on training data (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) spam (b) not spam
7. 7.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) many varied examples (d) wrong labels
8. 8.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
9. 9.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
10. 10.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
11. 11.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) blurry, unclear photos (d) checking labels twice
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) examples of every type (c) many varied examples (d) correct labels
14. 14.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it may get it wrong (c) it will shut down (d) it will be perfect
15. 15.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) examples of every type (d) copies of one photo
16. 16.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
17. 17.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
18. 18.Which of these is good for training a model? (a) testing on training data (b) checking labels twice (c) labels added at random (d) copies of one photo
19. 19.🔍 Which is the odd one out? (a) very few examples (b) wrong labels (c) clear, sharp examples (d) blurry, unclear photos
20. 20.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) photos in many lights (d) testing on training data
21. 21.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
22. 22.🔍 Which is the odd one out? (a) correct labels (b) wrong labels (c) many varied examples (d) photos in many lights
23. 23.Which of these is bad for training a model? (a) testing on training data (b) correct labels (c) examples of every type (d) new data for testing
24. 24.True or false: “testing on training data” is good for training a model. (a) True (b) False
25. 25.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
26. 26.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
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) not spam (b) spam
28. 28.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) better at its task (d) change colour
29. 29.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) correct labels (d) blurry, unclear photos
30. 30.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) charge faster (d) learn from data
31. 31.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) wrong labels (d) correct labels
32. 32.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
33. 33.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
34. 34.🏷️ 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
35. 35.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
36. 36.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
37. 37.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
38. 38.True or false: “wrong labels” is good for training a model. (a) False (b) True
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
40. 40.🏷️ 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

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