Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 164 · 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.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the price (d) the font size
2. 2.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) very few examples (d) wrong labels
3. 3.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) clear, sharp examples (d) very few examples
4. 4.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) examples of every type (d) labels added at random
5. 5.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) copies of one photo (d) photos in many lights
6. 6.Which of these is bad for training a model? (a) many varied examples (b) very few examples (c) checking labels twice (d) examples of every type
7. 7.🏷️ 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
8. 8.🔍 Which is the odd one out? (a) labels added at random (b) wrong labels (c) checking labels twice (d) only one kind of example
9. 9.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) blurry, unclear photos (d) new data for testing
10. 10.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) wrong labels (d) examples of every type
11. 11.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
12. 12.Which of these is bad for training a model? (a) examples of every type (b) copies of one photo (c) photos in many lights (d) clear, sharp examples
13. 13.🏷️ 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
14. 14.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) many varied examples (d) wrong labels
15. 15.🐶 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) a song about dogs (d) one single photo
16. 16.🧠 Machine learning is a way for computers to… (a) print pages (b) learn from data (c) charge faster (d) clean screens
17. 17.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) slower to switch on (d) change colour
18. 18.Which of these is good for training a model? (a) correct labels (b) blurry, unclear photos (c) very few examples (d) labels added at random
19. 19.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
20. 20.True or false: “wrong labels” is bad for training a model. (a) True (b) False
21. 21.🏷️ 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
22. 22.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
23. 23.Which of these is good for training a model? (a) only one kind of example (b) new data for testing (c) labels added at random (d) testing on training data
24. 24.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) only one kind of example (d) new data for testing
25. 25.True or false: “very few examples” is good for training a model. (a) False (b) True
26. 26.Which of these is good for training a model? (a) wrong labels (b) copies of one photo (c) very few examples (d) photos in many lights
27. 27.🥭 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
28. 28.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
29. 29.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
30. 30.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
31. 31.🏷️ 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
32. 32.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
33. 33.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
34. 34.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
35. 35.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) correct labels (d) blurry, unclear photos
36. 36.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
37. 37.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) examples of every type (d) testing on training data
38. 38.🏷️ 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
39. 39.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
40. 40.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training

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