Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 283 · 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.True or false: “checking labels twice” is good for training a model. (a) False (b) True
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
3. 3.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) labels added at random (d) testing on training data
4. 4.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
5. 5.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
6. 6.True or false: “labels added at random” is bad for training a model. (a) False (b) True
7. 7.🏷️ 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
8. 8.True or false: “correct labels” is bad for training a model. (a) False (b) True
9. 9.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will shut down (d) it will turn yellow
10. 10.Which of these is bad for training a model? (a) checking labels twice (b) examples of every type (c) labels added at random (d) new data for testing
11. 11.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) better at its task (d) change colour
12. 12.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) examples of every type (d) wrong labels
13. 13.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
14. 14.🔍 Which is the odd one out? (a) copies of one photo (b) new data for testing (c) correct labels (d) clear, sharp examples
15. 15.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
16. 16.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
17. 17.🐶 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
18. 18.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
19. 19.🏷️ 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
20. 20.🔍 Which is the odd one out? (a) photos in many lights (b) very few examples (c) only one kind of example (d) copies of one photo
21. 21.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
22. 22.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
25. 25.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) testing on training data (d) checking labels twice
27. 27.🔍 Which is the odd one out? (a) very few examples (b) clear, sharp examples (c) only one kind of example (d) testing on training data
28. 28.🏷️ 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
29. 29.Which of these is good for training a model? (a) wrong labels (b) correct labels (c) copies of one photo (d) very few examples
30. 30.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
31. 31.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
32. 32.True or false: the message “Share your OTP to get cashback” should be labelled “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) not spam (b) spam
34. 34.Which of these is good for training a model? (a) new data for testing (b) wrong labels (c) very few examples (d) only one kind of example
35. 35.🏷️ 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
36. 36.🔍 Which is the odd one out? (a) correct labels (b) clear, sharp examples (c) labels added at random (d) checking labels twice
37. 37.🔍 Which is the odd one out? (a) only one kind of example (b) testing on training data (c) new data for testing (d) wrong labels
38. 38.True or false: “correct labels” is good for training a model. (a) True (b) False
39. 39.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
40. 40.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training

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