Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 292 · 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) only one kind of example (b) clear, sharp examples (c) blurry, unclear photos (d) labels added at random
2. 2.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
3. 3.🏷️ 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
4. 4.🏷️ 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
5. 5.🔍 Which is the odd one out? (a) very few examples (b) wrong labels (c) copies of one photo (d) correct labels
6. 6.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
7. 7.🏷️ 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
8. 8.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
9. 9.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
10. 10.True or false: “many varied examples” is good for training a model. (a) False (b) True
11. 11.🏷️ 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
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
13. 13.🔍 Which is the odd one out? (a) many varied examples (b) only one kind of example (c) blurry, unclear photos (d) testing on training data
14. 14.True or false: “only one kind of example” is good for training a model. (a) False (b) True
15. 15.Which of these is good for training a model? (a) blurry, unclear photos (b) examples of every type (c) very few examples (d) only one kind of example
16. 16.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
17. 17.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) checking labels twice (d) testing on training data
18. 18.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) new data for testing (d) labels added at random
19. 19.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) new data for testing (d) blurry, unclear photos
20. 20.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) better at its task (d) forget everything
21. 21.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
22. 22.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
23. 23.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) wrong labels (d) new data for testing
24. 24.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) copies of one photo (d) photos in many lights
25. 25.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
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 shut down (c) it may get it wrong (d) it will be perfect
27. 27.🔍 Which is the odd one out? (a) checking labels twice (b) blurry, unclear photos (c) very few examples (d) labels added at random
28. 28.True or false: “examples of every type” is good for training a model. (a) False (b) True
29. 29.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
30. 30.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) checking labels twice (d) wrong labels
31. 31.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
32. 32.🔍 Which is the odd one out? (a) only one kind of example (b) wrong labels (c) examples of every type (d) very few examples
33. 33.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
34. 34.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
35. 35.🏷️ 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
36. 36.Which of these is good for training a model? (a) wrong labels (b) copies of one photo (c) many varied examples (d) testing on training data
37. 37.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) blurry, unclear photos (d) wrong labels
38. 38.Which of these is good for training a model? (a) wrong labels (b) photos in many lights (c) labels added at random (d) only one kind of example
39. 39.True or false: “examples of every type” is bad for training a model. (a) True (b) False
40. 40.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training

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