Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 372 · 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.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
2. 2.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
3. 3.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
4. 4.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) clear, sharp examples (d) correct labels
5. 5.🏷️ 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
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
7. 7.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) copies of one photo (d) clear, sharp examples
8. 8.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
9. 9.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
10. 10.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
11. 11.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) photos in many lights (d) blurry, unclear photos
12. 12.🔍 Which is the odd one out? (a) examples of every type (b) checking labels twice (c) wrong labels (d) photos in many lights
13. 13.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
14. 14.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
15. 15.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
16. 16.🔍 Which is the odd one out? (a) new data for testing (b) copies of one photo (c) checking labels twice (d) correct labels
17. 17.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) better at its task (d) change colour
18. 18.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
19. 19.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
20. 20.Which of these is good for training a model? (a) blurry, unclear photos (b) examples of every type (c) labels added at random (d) testing on training data
21. 21.Which of these is bad for training a model? (a) photos in many lights (b) labels added at random (c) many varied examples (d) clear, sharp examples
22. 22.True or false: “copies of one photo” is good for training a model. (a) True (b) False
23. 23.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) wrong labels (d) many varied examples
24. 24.Which of these is good for training a model? (a) correct labels (b) only one kind of example (c) testing on training data (d) very few examples
25. 25.🏷️ 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
26. 26.🥭 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 may get it wrong (d) it will shut down
27. 27.True or false: “new data for testing” is good for training a model. (a) False (b) True
28. 28.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
29. 29.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) checking labels twice (d) only one kind of example
30. 30.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) new data for testing (d) blurry, unclear photos
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
32. 32.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
33. 33.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) only one kind of example (d) examples of every type
34. 34.Which of these is bad for training a model? (a) copies of one photo (b) new data for testing (c) correct labels (d) photos in many lights
35. 35.True or false: “correct labels” is bad for training a model. (a) False (b) True
36. 36.True or false: “wrong labels” is bad for training a model. (a) False (b) True
37. 37.🔍 Which is the odd one out? (a) correct labels (b) only one kind of example (c) examples of every type (d) photos in many lights
38. 38.🏷️ 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
39. 39.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
40. 40.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training

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