Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 190 · 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 bad for training a model? (a) wrong labels (b) photos in many lights (c) examples of every type (d) clear, sharp examples
2. 2.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
3. 3.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
4. 4.🏷️ 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
5. 5.Which of these is good for training a model? (a) blurry, unclear photos (b) only one kind of example (c) copies of one photo (d) new data for testing
6. 6.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) labels added at random (d) many varied examples
8. 8.🏷️ 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
9. 9.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will turn yellow (c) it will shut down (d) it may get it wrong
10. 10.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
11. 11.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
12. 12.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) correct labels (d) very few examples
13. 13.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
14. 14.True or false: “wrong labels” is good for training a model. (a) False (b) True
15. 15.🏷️ 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
16. 16.🏷️ 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
17. 17.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) correct labels (d) very few examples
18. 18.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
19. 19.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the right answer tag (d) the font size
20. 20.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
21. 21.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
22. 22.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) labels added at random (d) examples of every type
24. 24.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
25. 25.🔍 Which is the odd one out? (a) very few examples (b) clear, sharp examples (c) labels added at random (d) only one kind of example
26. 26.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
27. 27.Which of these is bad for training a model? (a) photos in many lights (b) blurry, unclear photos (c) many varied examples (d) new data for testing
28. 28.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) photos in many lights (d) copies of one photo
29. 29.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) photos in many lights (d) only one kind of example
30. 30.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) copies of one photo (d) testing on training data
31. 31.Which of these is good for training a model? (a) only one kind of example (b) copies of one photo (c) blurry, unclear photos (d) many varied examples
32. 32.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
33. 33.True or false: “wrong labels” is bad for training a model. (a) False (b) True
34. 34.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
35. 35.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) testing on training data (d) photos in many lights
36. 36.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
37. 37.🔍 Which is the odd one out? (a) examples of every type (b) new data for testing (c) wrong labels (d) photos in many lights
38. 38.True or false: “photos in many lights” is good for training a model. (a) True (b) False
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
40. 40.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training

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