Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 147 · 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 turn yellow (b) it will shut down (c) it may get it wrong (d) it will be perfect
2. 2.Which of these is good for training a model? (a) testing on training data (b) correct labels (c) very few examples (d) labels added at random
3. 3.Which of these is bad for training a model? (a) correct labels (b) only one kind of example (c) new data for testing (d) many varied examples
4. 4.Which of these is bad for training a model? (a) correct labels (b) very few examples (c) many varied examples (d) new data for testing
5. 5.True or false: “new data for testing” is bad for training a model. (a) True (b) False
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
7. 7.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) blurry, unclear photos (d) clear, sharp examples
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
9. 9.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
10. 10.🔍 Which is the odd one out? (a) correct labels (b) clear, sharp examples (c) checking labels twice (d) labels added at random
11. 11.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
12. 12.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
13. 13.🔍 Which is the odd one out? (a) clear, sharp examples (b) photos in many lights (c) examples of every type (d) copies of one photo
14. 14.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the right answer tag (d) the font size
15. 15.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) checking labels twice (d) wrong labels
16. 16.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
17. 17.True or false: “very few examples” is good for training a model. (a) True (b) False
18. 18.🏷️ 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
19. 19.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
20. 20.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
21. 21.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
22. 22.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) examples of every type (d) only one kind of example
23. 23.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
24. 24.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
25. 25.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) checking labels twice (d) testing on training data
26. 26.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) labels added at random (d) checking labels twice
27. 27.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
28. 28.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
29. 29.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
30. 30.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
31. 31.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) only one kind of example (b) correct labels (c) wrong labels (d) labels added at random
33. 33.🔍 Which is the odd one out? (a) very few examples (b) correct labels (c) new data for testing (d) photos in many lights
34. 34.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
35. 35.🏷️ 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
36. 36.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
37. 37.Which of these is good for training a model? (a) photos in many lights (b) labels added at random (c) blurry, unclear photos (d) testing on training data
38. 38.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
39. 39.🔍 Which is the odd one out? (a) many varied examples (b) testing on training data (c) labels added at random (d) very few examples
40. 40.Which of these is bad for training a model? (a) testing on training data (b) examples of every type (c) many varied examples (d) clear, sharp examples

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