Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 567 · 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) blurry, unclear photos (b) correct labels (c) only one kind of example (d) copies of one photo
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
3. 3.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
4. 4.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the font size (d) the price
5. 5.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
6. 6.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) photos in many lights (d) copies of one photo
7. 7.True or false: “photos in many lights” is good for training a model. (a) True (b) False
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
9. 9.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) correct labels (d) testing on training data
10. 10.True or false: “only one kind of example” is good for training a model. (a) False (b) True
11. 11.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) labels added at random (d) only one kind of example
12. 12.Which of these is bad for training a model? (a) copies of one photo (b) examples of every type (c) photos in many lights (d) checking labels twice
13. 13.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) testing on training data (d) very few examples
14. 14.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
15. 15.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) blurry, unclear photos (d) correct labels
16. 16.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
17. 17.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
18. 18.Which of these is good for training a model? (a) blurry, unclear photos (b) only one kind of example (c) wrong labels (d) checking labels twice
19. 19.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
20. 20.🏷️ 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
21. 21.True or false: “testing on training data” is bad for training a model. (a) True (b) False
22. 22.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) print pages (d) learn from data
23. 23.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
24. 24.True or false: “new data for testing” is good for training a model. (a) False (b) True
25. 25.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
26. 26.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
27. 27.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) labels added at random (d) new data for testing
28. 28.🏷️ 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
29. 29.True or false: “new data for testing” is bad for training a model. (a) False (b) True
30. 30.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
31. 31.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
32. 32.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) blurry, unclear photos (d) photos in many lights
33. 33.True or false: “copies of one photo” is good for training a model. (a) True (b) False
34. 34.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
35. 35.🥭 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
36. 36.🏷️ 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
37. 37.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) photos in many lights (d) very few examples
38. 38.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
39. 39.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
40. 40.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False

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