Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 349 · 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.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
2. 2.True or false: “labels added at random” is good for training a model. (a) True (b) False
3. 3.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
4. 4.🔍 Which is the odd one out? (a) many varied examples (b) blurry, unclear photos (c) photos in many lights (d) checking labels twice
5. 5.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) many varied examples (d) wrong labels
7. 7.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) a list of rules only (d) many labelled photos
8. 8.Which of these is bad for training a model? (a) new data for testing (b) wrong labels (c) photos in many lights (d) many varied examples
9. 9.True or false: “examples of every type” is bad for training a model. (a) True (b) False
10. 10.True or false: “new data for testing” is good for training a model. (a) True (b) False
11. 11.🏷️ 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
12. 12.🔍 Which is the odd one out? (a) copies of one photo (b) many varied examples (c) new data for testing (d) correct labels
13. 13.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
14. 14.Which of these is good for training a model? (a) copies of one photo (b) examples of every type (c) testing on training data (d) very few examples
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
16. 16.True or false: “only one kind of example” is good for training a model. (a) True (b) False
17. 17.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
18. 18.True or false: “correct labels” is good for training a model. (a) False (b) True
19. 19.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
20. 20.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
21. 21.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
22. 22.Which of these is good for training a model? (a) correct labels (b) blurry, unclear photos (c) only one kind of example (d) wrong labels
23. 23.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) correct labels (d) labels added at random
24. 24.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
25. 25.🧠 Machine learning is a way for computers to… (a) print pages (b) clean screens (c) learn from data (d) charge faster
26. 26.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
27. 27.True or false: “testing on training data” is bad for training a model. (a) False (b) True
28. 28.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
29. 29.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
30. 30.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
31. 31.Which of these is bad for training a model? (a) clear, sharp examples (b) correct labels (c) checking labels twice (d) labels added at random
32. 32.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
33. 33.🏷️ 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
34. 34.🔍 Which is the odd one out? (a) checking labels twice (b) new data for testing (c) examples of every type (d) very few examples
35. 35.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) testing on training data (d) clear, sharp examples
36. 36.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
37. 37.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
38. 38.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
39. 39.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
40. 40.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True

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