Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 276 · 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 is the odd one out? (a) photos in many lights (b) only one kind of example (c) testing on training data (d) very few examples
2. 2.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
3. 3.True or false: “new data for testing” is bad for training a model. (a) True (b) False
4. 4.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
5. 5.Which of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) only one kind of example (d) many varied examples
6. 6.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) many varied examples (d) wrong labels
7. 7.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) blurry, unclear photos (d) checking labels twice
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) spam (b) not spam
9. 9.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
10. 10.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
11. 11.🏷️ 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
12. 12.Which of these is good for training a model? (a) copies of one photo (b) photos in many lights (c) blurry, unclear photos (d) only one kind of example
13. 13.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
14. 14.🥭 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
15. 15.🏷️ 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
16. 16.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) very few examples (d) photos in many lights
17. 17.🔍 Which is the odd one out? (a) very few examples (b) new data for testing (c) wrong labels (d) copies of one photo
18. 18.True or false: “correct labels” is good for training a model. (a) False (b) True
19. 19.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
20. 20.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) testing on training data (d) new data for testing
21. 21.🏷️ 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
22. 22.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
23. 23.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
24. 24.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
25. 25.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) a sticker on a laptop (d) the font size
26. 26.True or false: “many varied examples” is good for training a model. (a) True (b) False
27. 27.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) copies of one photo (d) correct labels
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.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) new data for testing (d) copies of one photo
30. 30.🏷️ 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
31. 31.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
32. 32.🏷️ 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
33. 33.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) examples of every type (d) many varied examples
35. 35.🏷️ 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
36. 36.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) wrong labels (d) correct labels
37. 37.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) new data for testing (d) only one kind of example
38. 38.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
39. 39.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
40. 40.Which of these is bad for training a model? (a) clear, sharp examples (b) wrong labels (c) correct labels (d) examples of every type

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