Machine Learning for Kids Worksheet

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

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