Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 599 · 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 is the odd one out? (a) blurry, unclear photos (b) many varied examples (c) only one kind of example (d) copies of one photo
2. 2.🔍 Which is the odd one out? (a) labels added at random (b) clear, sharp examples (c) checking labels twice (d) many varied examples
3. 3.True or false: “testing on training data” is good for training a model. (a) False (b) True
4. 4.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
5. 5.🏷️ 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
6. 6.Which of these is good for training a model? (a) only one kind of example (b) checking labels twice (c) very few examples (d) testing on training data
7. 7.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) correct labels (d) examples of every type
8. 8.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
9. 9.True or false: “only one kind of example” is good for training a model. (a) True (b) False
10. 10.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) new data for testing (d) copies of one photo
11. 11.🥭 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 will turn yellow (d) it may get it wrong
12. 12.True or false: “correct labels” is bad for training a model. (a) True (b) False
13. 13.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
14. 14.Which of these is bad for training a model? (a) wrong labels (b) clear, sharp examples (c) photos in many lights (d) checking labels twice
15. 15.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
16. 16.True or false: “very few examples” is bad for training a model. (a) True (b) False
17. 17.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) wrong labels (d) clear, sharp examples
18. 18.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
19. 19.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
20. 20.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) correct labels (d) wrong labels
21. 21.Which of these is good for training a model? (a) testing on training data (b) only one kind of example (c) many varied examples (d) wrong labels
22. 22.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
23. 23.Which of these is bad for training a model? (a) photos in many lights (b) only one kind of example (c) new data for testing (d) correct labels
24. 24.Which of these is bad for training a model? (a) examples of every type (b) many varied examples (c) blurry, unclear photos (d) checking labels twice
25. 25.🏷️ 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
26. 26.🏷️ 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
27. 27.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) very few examples (d) photos in many lights
28. 28.True or false: “examples of every type” is bad for training a model. (a) False (b) True
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
30. 30.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
31. 31.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
33. 33.🔍 Which is the odd one out? (a) photos in many lights (b) examples of every type (c) copies of one photo (d) correct labels
34. 34.True or false: “new data for testing” is good for training a model. (a) True (b) False
35. 35.True or false: “labels added at random” is good for training a model. (a) False (b) True
36. 36.True or false: “labels added at random” is bad for training a model. (a) False (b) True
37. 37.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) a sticker on a laptop (d) the font size
38. 38.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
39. 39.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) photos in many lights (d) checking labels twice
40. 40.Which of these is good for training a model? (a) only one kind of example (b) examples of every type (c) blurry, unclear photos (d) testing on training data

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