Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 312 · 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.🐶 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
2. 2.True or false: “labels added at random” is good for training a model. (a) False (b) True
3. 3.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) slower to switch on (d) change colour
4. 4.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) wrong labels (d) checking labels twice
5. 5.Which of these is bad for training a model? (a) examples of every type (b) checking labels twice (c) blurry, unclear photos (d) correct labels
6. 6.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
7. 7.🔍 Which is the odd one out? (a) testing on training data (b) checking labels twice (c) many varied examples (d) correct labels
8. 8.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) print pages (d) clean screens
9. 9.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
10. 10.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
11. 11.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
12. 12.Which of these is good for training a model? (a) examples of every type (b) blurry, unclear photos (c) only one kind of example (d) testing on training data
13. 13.🏷️ 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
14. 14.True or false: “copies of one photo” is good for training a model. (a) False (b) True
15. 15.True or false: “checking labels twice” is good for training a model. (a) True (b) False
16. 16.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
17. 17.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) very few examples (d) clear, sharp examples
18. 18.True or false: “new data for testing” is good for training a model. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) new data for testing (d) testing on training data
20. 20.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) clear, sharp examples (d) wrong labels
21. 21.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
22. 22.True or false: “correct labels” is good for training a model. (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) examples of every type (b) correct labels (c) wrong labels (d) clear, sharp examples
24. 24.🏷️ 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
25. 25.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) clear, sharp examples (d) examples of every type
26. 26.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
27. 27.🏷️ 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
28. 28.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
29. 29.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
30. 30.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) very few examples (d) photos in many lights
31. 31.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
32. 32.Which of these is good for training a model? (a) only one kind of example (b) clear, sharp examples (c) very few examples (d) copies of one photo
33. 33.Which of these is bad for training a model? (a) new data for testing (b) testing on training data (c) photos in many lights (d) correct labels
34. 34.True or false: “only one kind of example” is good for training a model. (a) False (b) True
35. 35.🔍 Which is the odd one out? (a) blurry, unclear photos (b) correct labels (c) new data for testing (d) examples of every type
36. 36.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
37. 37.🔍 Which is the odd one out? (a) photos in many lights (b) correct labels (c) many varied examples (d) copies of one photo
38. 38.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) photos in many lights (d) blurry, unclear photos
39. 39.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) a sticker on a laptop (d) the right answer tag
40. 40.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training

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