Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 259 · 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: “checking labels twice” is bad for training a model. (a) True (b) False
2. 2.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) only one kind of example (d) checking labels twice
3. 3.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) a sticker on a laptop (d) the right answer tag
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
5. 5.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
6. 6.Which of these is bad for training a model? (a) checking labels twice (b) wrong labels (c) photos in many lights (d) clear, sharp examples
7. 7.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
8. 8.🏷️ 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
9. 9.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
10. 10.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
11. 11.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) photos in many lights (d) new data for testing
12. 12.Which of these is good for training a model? (a) wrong labels (b) photos in many lights (c) testing on training data (d) copies of one photo
13. 13.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
14. 14.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
15. 15.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) checking labels twice (d) wrong labels
16. 16.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) testing on training data (d) clear, sharp examples
17. 17.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
18. 18.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
19. 19.Which of these is bad for training a model? (a) very few examples (b) checking labels twice (c) correct labels (d) new data for testing
20. 20.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
22. 22.🏷️ 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
23. 23.🔍 Which is the odd one out? (a) copies of one photo (b) wrong labels (c) very few examples (d) clear, sharp examples
24. 24.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
25. 25.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) labels added at random (d) testing on training data
27. 27.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
28. 28.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
29. 29.Which of these is bad for training a model? (a) checking labels twice (b) clear, sharp examples (c) blurry, unclear photos (d) correct labels
30. 30.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will turn yellow (d) it will shut down
31. 31.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
32. 32.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
33. 33.True or false: “very few examples” is good for training a model. (a) True (b) False
34. 34.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) labels added at random (d) photos in many lights
35. 35.🏷️ 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
36. 36.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) many varied examples (d) copies of one photo
37. 37.Which of these is good for training a model? (a) wrong labels (b) correct labels (c) testing on training data (d) copies of one photo
38. 38.True or false: “only one kind of example” is good for training a model. (a) True (b) False
39. 39.🔍 Which is the odd one out? (a) very few examples (b) photos in many lights (c) wrong labels (d) blurry, unclear photos
40. 40.🏷️ 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

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