Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 176 · 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) a song about dogs (c) many labelled photos (d) one single photo
2. 2.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
3. 3.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
4. 4.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) checking labels twice (d) new data for testing
5. 5.🏷️ 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
6. 6.Which of these is bad for training a model? (a) labels added at random (b) new data for testing (c) photos in many lights (d) checking labels twice
7. 7.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
8. 8.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) new data for testing (d) wrong labels
9. 9.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
10. 10.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) the font size (d) a sticker on a laptop
11. 11.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) blurry, unclear photos (b) copies of one photo (c) new data for testing (d) wrong labels
13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
14. 14.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
15. 15.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
16. 16.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
17. 17.True or false: “many varied examples” is bad for training a model. (a) True (b) False
18. 18.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) many varied examples (d) copies of one photo
19. 19.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) wrong labels (d) checking labels twice
20. 20.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
21. 21.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
22. 22.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
23. 23.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
24. 24.🏷️ 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
25. 25.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it may get it wrong (c) it will turn yellow (d) it will shut down
26. 26.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
27. 27.🔍 Which is the odd one out? (a) photos in many lights (b) examples of every type (c) new data for testing (d) very few examples
28. 28.Which of these is good for training a model? (a) clear, sharp examples (b) only one kind of example (c) copies of one photo (d) very few examples
29. 29.Which of these is good for training a model? (a) labels added at random (b) copies of one photo (c) wrong labels (d) checking labels twice
30. 30.True or false: “examples of every type” is bad for training a model. (a) False (b) True
31. 31.True or false: “very few examples” is good for training a model. (a) True (b) False
32. 32.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) correct labels (d) very few examples
33. 33.True or false: “checking labels twice” is good 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? “You won a free phone! Click now!” (a) not spam (b) spam
35. 35.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
36. 36.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) only one kind of example (d) blurry, unclear photos
37. 37.True or false: “only one kind of example” is good for training a model. (a) True (b) False
38. 38.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
39. 39.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
40. 40.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) correct labels (d) wrong labels

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