Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 481 · 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 good for training a model? (a) blurry, unclear photos (b) photos in many lights (c) copies of one photo (d) labels added at random
2. 2.Which of these is bad for training a model? (a) clear, sharp examples (b) examples of every type (c) photos in many lights (d) copies of one photo
3. 3.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) checking labels twice (d) wrong labels
4. 4.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
5. 5.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) photos in many lights (d) testing on training data
6. 6.🏷️ 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
7. 7.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
8. 8.🔍 Which is the odd one out? (a) labels added at random (b) correct labels (c) photos in many lights (d) checking labels twice
9. 9.Which of these is good for training a model? (a) clear, sharp examples (b) wrong labels (c) only one kind of example (d) testing on training data
10. 10.🔍 Which is the odd one out? (a) testing on training data (b) clear, sharp examples (c) very few examples (d) only one kind of example
11. 11.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
12. 12.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
13. 13.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
14. 14.🏷️ 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
15. 15.True or false: “many varied examples” is bad for training a model. (a) True (b) False
16. 16.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
17. 17.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
18. 18.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) wrong labels (d) examples of every type
19. 19.True or false: “examples of every type” is good for training a model. (a) False (b) True
20. 20.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a song about dogs (d) a list of rules only
21. 21.🏷️ 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
22. 22.🔍 Which is the odd one out? (a) testing on training data (b) new data for testing (c) labels added at random (d) blurry, unclear photos
23. 23.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
24. 24.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
26. 26.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
27. 27.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
28. 28.True or false: “correct labels” is bad for training a model. (a) True (b) False
29. 29.🔍 Which is the odd one out? (a) checking labels twice (b) labels added at random (c) copies of one photo (d) wrong labels
30. 30.🏷️ 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
31. 31.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) very few examples (d) correct labels
32. 32.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
33. 33.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
34. 34.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
35. 35.🏷️ 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
36. 36.🏷️ What is a “label” in machine learning? (a) the font size (b) the right answer tag (c) a sticker on a laptop (d) the price
37. 37.🏷️ 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
38. 38.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
39. 39.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
40. 40.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will turn yellow (d) it will be perfect

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