Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 526 · 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.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will shut down (c) it will turn yellow (d) it will be perfect
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
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) not spam (b) spam
4. 4.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) photos in many lights (d) copies of one photo
5. 5.Which of these is bad for training a model? (a) checking labels twice (b) many varied examples (c) photos in many lights (d) copies of one photo
6. 6.True or false: “correct labels” is bad for training a model. (a) True (b) False
7. 7.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) examples of every type (d) only one kind of example
8. 8.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
9. 9.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
10. 10.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
11. 11.🏷️ 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
12. 12.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) many varied examples (c) very few examples (d) only one kind of example
14. 14.True or false: “copies of one photo” is good for training a model. (a) True (b) False
15. 15.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
16. 16.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) photos in many lights (d) labels added at random
17. 17.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
18. 18.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) one single photo (d) a song about dogs
19. 19.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) charge faster (d) print pages
20. 20.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
21. 21.🔍 Which is the odd one out? (a) photos in many lights (b) examples of every type (c) clear, sharp examples (d) blurry, unclear photos
22. 22.Which of these is bad for training a model? (a) new data for testing (b) labels added at random (c) checking labels twice (d) examples of every type
23. 23.🏷️ 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
24. 24.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
25. 25.Which of these is bad for training a model? (a) new data for testing (b) blurry, unclear photos (c) examples of every type (d) photos in many lights
26. 26.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
27. 27.Which of these is good for training a model? (a) clear, sharp examples (b) only one kind of example (c) labels added at random (d) testing on training data
28. 28.🏷️ 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
29. 29.🏷️ 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
30. 30.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) slower to switch on (d) better at its task
31. 31.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) testing on training data (d) correct labels
33. 33.Which of these is good for training a model? (a) correct labels (b) blurry, unclear photos (c) wrong labels (d) testing on training data
34. 34.🏷️ 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) not spam (b) spam
35. 35.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
36. 36.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
37. 37.True or false: “photos in many lights” is good for training a model. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) checking labels twice (b) new data for testing (c) photos in many lights (d) testing on training data
39. 39.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) many varied examples (d) only one kind of example
40. 40.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) very few examples (d) correct labels

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