Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 184 · 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) very few examples (b) new data for testing (c) only one kind of example (d) labels added at random
2. 2.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
3. 3.🔍 Which is the odd one out? (a) correct labels (b) copies of one photo (c) checking labels twice (d) many varied examples
4. 4.🏷️ 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
5. 5.🏷️ 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
6. 6.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
8. 8.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
9. 9.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
10. 10.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) very few examples (d) only one kind of example
11. 11.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) clear, sharp examples (d) only one kind of example
12. 12.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will be perfect (d) it will shut down
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) correct labels (c) new data for testing (d) examples of every type
14. 14.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) clean screens (d) charge faster
15. 15.🐶 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
16. 16.Which of these is bad for training a model? (a) copies of one photo (b) examples of every type (c) many varied examples (d) checking labels twice
17. 17.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) only one kind of example (d) correct labels
18. 18.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
19. 19.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
20. 20.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
21. 21.🏷️ 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
22. 22.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) forget everything (d) slower to switch on
23. 23.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
24. 24.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) new data for testing (d) only one kind of example
25. 25.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
26. 26.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
27. 27.True or false: “new data for testing” is bad for training a model. (a) False (b) True
28. 28.🏷️ 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
29. 29.True or false: “labels added at random” is good for training a model. (a) False (b) True
30. 30.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
31. 31.True or false: “testing on training data” is good for training a model. (a) True (b) False
32. 32.Which of these is bad for training a model? (a) clear, sharp examples (b) correct labels (c) very few examples (d) examples of every type
33. 33.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) examples of every type (d) blurry, unclear photos
34. 34.🏷️ 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
35. 35.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) photos in many lights (d) very few examples
36. 36.🏷️ 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
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.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
39. 39.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
40. 40.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training

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