Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 342 · 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) many varied examples (b) very few examples (c) photos in many lights (d) correct labels
2. 2.Which of these is good for training a model? (a) testing on training data (b) checking labels twice (c) only one kind of example (d) wrong labels
3. 3.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
5. 5.Which of these is good for training a model? (a) wrong labels (b) many varied examples (c) only one kind of example (d) copies of one photo
6. 6.🏷️ 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
7. 7.True or false: “labels added at random” is good for training a model. (a) False (b) True
8. 8.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
9. 9.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) examples of every type (d) copies of one photo
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
11. 11.🔍 Which is the odd one out? (a) copies of one photo (b) new data for testing (c) wrong labels (d) very few examples
12. 12.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) wrong labels (d) correct labels
13. 13.🏷️ 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
14. 14.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) new data for testing (d) copies of one photo
15. 15.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
16. 16.True or false: “photos in many lights” is good for training a model. (a) False (b) True
17. 17.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
18. 18.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) very few examples (d) checking labels twice
19. 19.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
20. 20.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
21. 21.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the price (d) the font size
22. 22.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
23. 23.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
24. 24.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) photos in many lights (d) clear, sharp examples
25. 25.Which of these is bad for training a model? (a) correct labels (b) labels added at random (c) examples of every type (d) photos in many lights
26. 26.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) correct labels
27. 27.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will turn yellow (d) it will shut down
28. 28.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) blurry, unclear photos (d) photos in many lights
29. 29.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
30. 30.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
31. 31.Which of these is bad for training a model? (a) wrong labels (b) new data for testing (c) photos in many lights (d) examples of every type
32. 32.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
33. 33.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) clear, sharp examples (d) blurry, unclear photos
34. 34.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
35. 35.True or false: “testing on training data” is good for training a model. (a) True (b) False
36. 36.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
37. 37.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
38. 38.🏷️ 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
39. 39.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) a song about dogs (d) one single photo
40. 40.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training

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