Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 452 · 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: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
2. 2.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
3. 3.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) learn from data (d) print pages
4. 4.🏷️ 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
5. 5.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
6. 6.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) only one kind of example (d) checking labels twice
7. 7.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) many varied examples (d) very few examples
8. 8.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
9. 9.🏷️ 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
10. 10.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
11. 11.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
12. 12.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) many labelled photos (d) a list of rules only
13. 13.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
14. 14.🔍 Which is the odd one out? (a) clear, sharp examples (b) blurry, unclear photos (c) new data for testing (d) examples of every type
15. 15.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
16. 16.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
17. 17.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
18. 18.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) many varied examples (d) testing on training data
19. 19.Which of these is good for training a model? (a) clear, sharp examples (b) very few examples (c) labels added at random (d) copies of one photo
20. 20.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) examples of every type (d) copies of one photo
22. 22.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
23. 23.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
24. 24.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) very few examples (d) blurry, unclear photos
25. 25.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
26. 26.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
27. 27.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
28. 28.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
29. 29.🏷️ 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
30. 30.True or false: “very few examples” is bad for training a model. (a) True (b) False
31. 31.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
32. 32.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
33. 33.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
34. 34.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) photos in many lights (d) very few examples
35. 35.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
36. 36.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the right answer tag (d) the font size
37. 37.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
38. 38.🏷️ 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
39. 39.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) correct labels (d) wrong labels
40. 40.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) clear, sharp examples (d) checking labels twice

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