Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 97 · 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 bad for training a model? (a) examples of every type (b) labels added at random (c) photos in many lights (d) correct labels
2. 2.🏷️ 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
3. 3.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
4. 4.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will turn yellow (d) it will be perfect
5. 5.🔍 Which is the odd one out? (a) only one kind of example (b) correct labels (c) testing on training data (d) wrong labels
6. 6.True or false: “many varied examples” is bad for training a model. (a) True (b) False
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
8. 8.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) new data for testing (d) wrong labels
9. 9.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
11. 11.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
12. 12.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
13. 13.🔍 Which is the odd one out? (a) blurry, unclear photos (b) only one kind of example (c) photos in many lights (d) wrong labels
14. 14.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) one single photo (d) many labelled photos
15. 15.Which of these is bad for training a model? (a) checking labels twice (b) many varied examples (c) clear, sharp examples (d) wrong labels
16. 16.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
17. 17.True or false: “copies of one photo” is good for training a model. (a) True (b) False
18. 18.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
19. 19.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
20. 20.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
21. 21.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) testing on training data (d) photos in many lights
22. 22.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) copies of one photo (d) many varied examples
23. 23.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
24. 24.True or false: “wrong labels” is bad for training a model. (a) False (b) True
25. 25.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
26. 26.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) checking labels twice (b) very few examples (c) clear, sharp examples (d) examples of every type
28. 28.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) very few examples (d) checking labels twice
29. 29.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) wrong labels (d) clear, sharp examples
30. 30.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
31. 31.🔍 Which is the odd one out? (a) checking labels twice (b) clear, sharp examples (c) only one kind of example (d) new data for testing
32. 32.🏷️ 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
33. 33.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
34. 34.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) the price (d) a sticker on a laptop
35. 35.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
36. 36.🔍 Which is the odd one out? (a) testing on training data (b) checking labels twice (c) labels added at random (d) wrong labels
37. 37.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) checking labels twice (d) copies of one photo
38. 38.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) blurry, unclear photos (d) correct labels
39. 39.Which of these is good for training a model? (a) only one kind of example (b) new data for testing (c) copies of one photo (d) wrong labels
40. 40.🏷️ 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

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