Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 19 · 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) correct labels (b) checking labels twice (c) blurry, unclear photos (d) new data for testing
2. 2.🏷️ 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
3. 3.🔍 Which is the odd one out? (a) blurry, unclear photos (b) copies of one photo (c) only one kind of example (d) many varied examples
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) one single photo (d) many labelled photos
5. 5.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
6. 6.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
7. 7.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
8. 8.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
9. 9.True or false: “wrong labels” is bad for training a model. (a) True (b) False
10. 10.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
11. 11.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
12. 12.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) many varied examples (d) wrong labels
13. 13.Which of these is bad for training a model? (a) very few examples (b) photos in many lights (c) examples of every type (d) checking labels twice
14. 14.Which of these is bad for training a model? (a) photos in many lights (b) only one kind of example (c) correct labels (d) many varied examples
15. 15.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
16. 16.True or false: “examples of every type” is good for training a model. (a) False (b) True
17. 17.🏷️ 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
18. 18.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) correct labels (b) wrong labels (c) clear, sharp examples (d) new data for testing
20. 20.🏷️ 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
21. 21.🏷️ 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
22. 22.🏷️ 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
23. 23.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
24. 24.True or false: “new data for testing” is bad for training a model. (a) False (b) True
25. 25.True or false: “only one kind of example” 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) spam (b) not spam
27. 27.Which of these is bad for training a model? (a) new data for testing (b) labels added at random (c) checking labels twice (d) correct labels
28. 28.🔍 Which is the odd one out? (a) very few examples (b) examples of every type (c) wrong labels (d) testing on training data
29. 29.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) clear, sharp examples (d) testing on training data
30. 30.🔍 Which is the odd one out? (a) blurry, unclear photos (b) new data for testing (c) very few examples (d) only one kind of example
31. 31.Which of these is good for training a model? (a) checking labels twice (b) copies of one photo (c) labels added at random (d) blurry, unclear photos
32. 32.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
33. 33.Which of these is bad for training a model? (a) many varied examples (b) testing on training data (c) new data for testing (d) examples of every type
34. 34.True or false: “copies of one photo” is good for training a model. (a) False (b) True
35. 35.🏷️ 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
36. 36.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
37. 37.Which of these is good for training a model? (a) photos in many lights (b) testing on training data (c) labels added at random (d) very few examples
38. 38.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the price (d) the right answer tag
39. 39.🔍 Which is the odd one out? (a) very few examples (b) copies of one photo (c) wrong labels (d) clear, sharp examples
40. 40.Which of these is good for training a model? (a) clear, sharp examples (b) testing on training data (c) wrong labels (d) very few examples

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