Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 412 · 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.True or false: “testing on training data” is good for training a model. (a) True (b) False
2. 2.Which of these is good for training a model? (a) copies of one photo (b) many varied examples (c) labels added at random (d) wrong labels
3. 3.Which of these is good for training a model? (a) only one kind of example (b) checking labels twice (c) wrong labels (d) copies of one photo
4. 4.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
5. 5.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
6. 6.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
7. 7.True or false: “very few examples” is good for training a model. (a) True (b) False
8. 8.Which of these is bad for training a model? (a) photos in many lights (b) copies of one photo (c) many varied examples (d) clear, sharp examples
9. 9.True or false: “wrong labels” is bad for training a model. (a) False (b) True
10. 10.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) better at its task (d) change colour
11. 11.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
12. 12.🔍 Which is the odd one out? (a) copies of one photo (b) many varied examples (c) examples of every type (d) checking labels twice
13. 13.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) correct labels (d) very few examples
14. 14.True or false: “labels added at random” is good for training a model. (a) True (b) False
15. 15.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
16. 16.Which of these is bad for training a model? (a) photos in many lights (b) new data for testing (c) wrong labels (d) clear, sharp examples
17. 17.True or false: “photos in many lights” is good for training a model. (a) True (b) False
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
19. 19.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) checking labels twice (d) blurry, unclear photos
20. 20.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
21. 21.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
22. 22.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
23. 23.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) many labelled photos (d) a song about dogs
24. 24.🔍 Which is the odd one out? (a) blurry, unclear photos (b) checking labels twice (c) correct labels (d) examples of every type
25. 25.🏷️ 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
26. 26.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
27. 27.🏷️ 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
28. 28.Which of these is bad for training a model? (a) new data for testing (b) testing on training data (c) correct labels (d) photos in many lights
29. 29.Which of these is bad for training a model? (a) only one kind of example (b) new data for testing (c) correct labels (d) checking labels twice
30. 30.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) testing on training data (d) clear, sharp examples
32. 32.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
33. 33.🏷️ 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
34. 34.Which of these is good for training a model? (a) only one kind of example (b) labels added at random (c) copies of one photo (d) correct labels
35. 35.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
36. 36.🏷️ 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
37. 37.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
38. 38.True or false: “new data for testing” is bad for training a model. (a) True (b) False
39. 39.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
40. 40.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True

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