Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 401 · 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) correct labels (b) blurry, unclear photos (c) wrong labels (d) testing on training data
2. 2.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will be perfect (d) it will shut down
3. 3.🏷️ 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
4. 4.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) a sticker on a laptop (d) the right answer tag
5. 5.True or false: “labels added at random” is bad for training a model. (a) False (b) True
6. 6.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
7. 7.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
8. 8.Which of these is good for training a model? (a) only one kind of example (b) many varied examples (c) very few examples (d) testing on training data
9. 9.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) new data for testing (d) blurry, unclear photos
10. 10.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
11. 11.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
12. 12.🔍 Which is the odd one out? (a) clear, sharp examples (b) correct labels (c) very few examples (d) many varied examples
13. 13.🏷️ 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
14. 14.Which of these is good for training a model? (a) very few examples (b) copies of one photo (c) correct labels (d) labels added at random
15. 15.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
16. 16.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) better at its task (d) change colour
17. 17.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
18. 18.🏷️ 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) spam (b) not spam
19. 19.🏷️ 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
20. 20.🔍 Which is the odd one out? (a) checking labels twice (b) wrong labels (c) labels added at random (d) only one kind of example
21. 21.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
22. 22.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
23. 23.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
24. 24.🔍 Which is the odd one out? (a) correct labels (b) testing on training data (c) many varied examples (d) clear, sharp examples
25. 25.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
26. 26.Which of these is good for training a model? (a) examples of every type (b) testing on training data (c) blurry, unclear photos (d) labels added at random
27. 27.Which of these is bad for training a model? (a) many varied examples (b) very few examples (c) examples of every type (d) correct labels
28. 28.True or false: “wrong labels” is bad for training a model. (a) False (b) True
29. 29.Which of these is bad for training a model? (a) correct labels (b) blurry, unclear photos (c) new data for testing (d) clear, sharp examples
30. 30.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
31. 31.True or false: “copies of one photo” is good for training a model. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) checking labels twice (b) photos in many lights (c) blurry, unclear photos (d) many varied examples
33. 33.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) examples of every type (d) labels added at random
34. 34.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) one single photo (d) many labelled photos
35. 35.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
36. 36.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) print pages (d) charge faster
37. 37.Which of these is bad for training a model? (a) testing on training data (b) checking labels twice (c) many varied examples (d) photos in many lights
38. 38.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
39. 39.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
40. 40.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training

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