Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 568 · 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) new data for testing (b) copies of one photo (c) very few examples (d) only one kind of example
2. 2.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
3. 3.🏷️ 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
4. 4.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
5. 5.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) wrong labels (d) photos in many lights
6. 6.True or false: “new data for testing” is bad for training a model. (a) False (b) True
7. 7.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
8. 8.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) checking labels twice (d) only one kind of example
9. 9.🏷️ 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
10. 10.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
11. 11.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) very few examples (d) new data for testing
12. 12.🔍 Which is the odd one out? (a) only one kind of example (b) examples of every type (c) many varied examples (d) clear, sharp examples
13. 13.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) photos in many lights (d) very few examples
14. 14.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) blurry, unclear photos (d) correct labels
15. 15.Which of these is bad for training a model? (a) labels added at random (b) clear, sharp examples (c) correct labels (d) examples of every type
16. 16.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
18. 18.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) blurry, unclear photos (d) testing on training data
19. 19.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
20. 20.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
21. 21.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
22. 22.Which of these is bad for training a model? (a) many varied examples (b) copies of one photo (c) checking labels twice (d) correct labels
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 is the odd one out? (a) blurry, unclear photos (b) new data for testing (c) labels added at random (d) wrong labels
25. 25.True or false: “correct labels” is bad 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) spam (b) not spam
27. 27.True or false: “labels added at random” is bad for training a model. (a) False (b) True
28. 28.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
29. 29.True or false: “very few examples” is good for training a model. (a) False (b) True
30. 30.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
31. 31.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
32. 32.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
33. 33.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
34. 34.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
35. 35.🔍 Which is the odd one out? (a) labels added at random (b) correct labels (c) many varied examples (d) clear, sharp examples
36. 36.🔍 Which is the odd one out? (a) only one kind of example (b) many varied examples (c) copies of one photo (d) wrong labels
37. 37.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) testing on training data (d) checking labels twice
38. 38.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on
39. 39.🔍 Which is the odd one out? (a) testing on training data (b) photos in many lights (c) labels added at random (d) very few examples
40. 40.True or false: “wrong labels” is good for training a model. (a) False (b) True

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