Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 403 · 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) only one kind of example (b) testing on training data (c) labels added at random (d) photos in many lights
2. 2.True or false: “very few examples” is good for training a model. (a) False (b) True
3. 3.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
4. 4.Which of these is bad for training a model? (a) examples of every type (b) correct labels (c) new data for testing (d) labels added at random
5. 5.True or false: “examples of every type” is good for training a model. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) labels added at random (b) many varied examples (c) examples of every type (d) correct labels
7. 7.🔍 Which is the odd one out? (a) blurry, unclear photos (b) many varied examples (c) clear, sharp examples (d) checking labels twice
8. 8.True or false: “correct labels” is good for training a model. (a) True (b) False
9. 9.📚 Usually, more good examples make a model… (a) slower to switch on (b) better at its task (c) forget everything (d) change colour
10. 10.🔍 Which is the odd one out? (a) wrong labels (b) correct labels (c) only one kind of example (d) very few examples
11. 11.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
12. 12.🔍 Which is the odd one out? (a) copies of one photo (b) correct labels (c) many varied examples (d) photos in many lights
13. 13.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
14. 14.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
15. 15.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
16. 16.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) checking labels twice (d) wrong labels
17. 17.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) testing on training data (d) many varied examples
18. 18.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
19. 19.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
20. 20.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
21. 21.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
22. 22.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) new data for testing (d) wrong labels
23. 23.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) wrong labels (d) new data for testing
24. 24.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
25. 25.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) clean screens (d) print pages
26. 26.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the right answer tag (d) the font size
27. 27.Which of these is good for training a model? (a) testing on training data (b) correct labels (c) copies of one photo (d) wrong labels
28. 28.Which of these is bad for training a model? (a) new data for testing (b) very few examples (c) clear, sharp examples (d) checking labels twice
29. 29.🏷️ 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
30. 30.🏷️ 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
31. 31.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) new data for testing (d) copies of one photo
32. 32.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
33. 33.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
34. 34.True or false: “labels added at random” is bad for training a model. (a) False (b) True
35. 35.🔍 Which is the odd one out? (a) checking labels twice (b) copies of one photo (c) blurry, unclear photos (d) wrong labels
36. 36.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
37. 37.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
38. 38.🥭 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 may get it wrong (d) it will turn yellow
39. 39.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) very few examples (d) photos in many lights
40. 40.🏷️ 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

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