Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 236 · 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) testing on training data (b) checking labels twice (c) photos in many lights (d) many varied examples
2. 2.True or false: “many varied examples” is good for training a model. (a) True (b) False
3. 3.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) learn from data (d) clean screens
4. 4.🐶 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
5. 5.Which of these is bad for training a model? (a) checking labels twice (b) photos in many lights (c) very few examples (d) clear, sharp examples
6. 6.Which of these is good for training a model? (a) only one kind of example (b) checking labels twice (c) blurry, unclear photos (d) very few examples
7. 7.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
8. 8.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) checking labels twice (d) only one kind of example
9. 9.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
10. 10.Which of these is bad for training a model? (a) wrong labels (b) many varied examples (c) examples of every type (d) correct labels
11. 11.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
12. 12.🔍 Which is the odd one out? (a) blurry, unclear photos (b) correct labels (c) labels added at random (d) very few examples
13. 13.Which of these is bad for training a model? (a) copies of one photo (b) checking labels twice (c) clear, sharp examples (d) examples of every type
14. 14.True or false: “wrong labels” is good for training a model. (a) False (b) True
15. 15.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
16. 16.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
17. 17.True or false: “examples of every type” is bad for training a model. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) new data for testing (b) wrong labels (c) many varied examples (d) checking labels twice
19. 19.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) correct labels (d) many varied examples
20. 20.Which of these is good for training a model? (a) labels added at random (b) correct labels (c) copies of one photo (d) wrong labels
21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
22. 22.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it may get it wrong (d) it will turn yellow
23. 23.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
24. 24.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
25. 25.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) labels added at random (d) photos in many lights
26. 26.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
27. 27.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
28. 28.🏷️ 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
29. 29.Which of these is good for training a model? (a) very few examples (b) clear, sharp examples (c) wrong labels (d) copies of one photo
30. 30.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
31. 31.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
32. 32.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
33. 33.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) copies of one photo (d) testing on training data
34. 34.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) many varied examples (d) only one kind of example
35. 35.🏷️ 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
36. 36.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) slower to switch on (d) better at its task
37. 37.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
38. 38.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
39. 39.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
40. 40.🔍 Which is the odd one out? (a) only one kind of example (b) many varied examples (c) testing on training data (d) copies of one photo

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