Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 168 · 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.🥭 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
2. 2.Which of these is bad for training a model? (a) blurry, unclear photos (b) examples of every type (c) correct labels (d) new data for testing
3. 3.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
4. 4.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
5. 5.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
6. 6.🏷️ What is a “label” in machine learning? (a) the font size (b) the right answer tag (c) the price (d) a sticker on a laptop
7. 7.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
8. 8.Which of these is bad for training a model? (a) correct labels (b) copies of one photo (c) clear, sharp examples (d) examples of every type
9. 9.🔍 Which is the odd one out? (a) testing on training data (b) only one kind of example (c) blurry, unclear photos (d) new data for testing
10. 10.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
11. 11.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) one single photo (d) a song about dogs
12. 12.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
13. 13.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) photos in many lights (d) checking labels twice
14. 14.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) wrong labels (d) clear, sharp examples
15. 15.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
16. 16.True or false: “many varied examples” is bad for training a model. (a) True (b) False
17. 17.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) copies of one photo (d) very few examples
19. 19.🏷️ 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
20. 20.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
21. 21.Which of these is bad for training a model? (a) new data for testing (b) wrong labels (c) clear, sharp examples (d) many varied examples
22. 22.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
23. 23.🏷️ 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
24. 24.🔍 Which is the odd one out? (a) checking labels twice (b) clear, sharp examples (c) correct labels (d) only one kind of example
25. 25.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) wrong labels (d) examples of every type
26. 26.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) forget everything (d) better at its task
27. 27.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) testing on training data (d) photos in many lights
28. 28.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) checking labels twice (d) labels added at random
29. 29.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
30. 30.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
31. 31.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
32. 32.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
33. 33.True or false: “labels added at random” is bad for training a model. (a) True (b) False
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
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) spam (b) not spam
36. 36.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
37. 37.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) testing on training data (d) photos in many lights
38. 38.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) very few examples (d) checking labels twice
39. 39.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
40. 40.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) copies of one photo (d) correct labels

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