Machine Learning for Kids Worksheet

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

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