Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 93 · 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) new data for testing (b) labels added at random (c) clear, sharp examples (d) photos in many lights
2. 2.Which of these is good for training a model? (a) testing on training data (b) blurry, unclear photos (c) photos in many lights (d) only one kind of example
3. 3.True or false: “many varied examples” is bad for training a model. (a) True (b) False
4. 4.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
5. 5.True or false: “very few examples” is good for training a model. (a) True (b) False
6. 6.🏷️ 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
7. 7.🔍 Which is the odd one out? (a) wrong labels (b) copies of one photo (c) only one kind of example (d) clear, sharp examples
8. 8.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
9. 9.🏷️ 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
10. 10.🏷️ 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
11. 11.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
12. 12.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) examples of every type (d) wrong labels
13. 13.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
14. 14.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) slower to switch on (d) better at its task
15. 15.Which of these is bad for training a model? (a) clear, sharp examples (b) examples of every type (c) checking labels twice (d) wrong labels
16. 16.True or false: “correct labels” is bad for training a model. (a) True (b) False
17. 17.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) wrong labels (d) checking labels twice
18. 18.True or false: “testing on training data” is bad for training a model. (a) True (b) False
19. 19.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
20. 20.Which of these is bad for training a model? (a) clear, sharp examples (b) labels added at random (c) photos in many lights (d) examples of every type
21. 21.🏷️ 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
22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
23. 23.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) print pages (d) charge faster
24. 24.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) wrong labels (d) clear, sharp examples
25. 25.🏷️ 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
26. 26.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
27. 27.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) new data for testing (d) copies of one photo
28. 28.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
29. 29.🔍 Which is the odd one out? (a) new data for testing (b) only one kind of example (c) many varied examples (d) clear, sharp examples
30. 30.🔍 Which is the odd one out? (a) photos in many lights (b) copies of one photo (c) testing on training data (d) very few examples
31. 31.Which of these is good for training a model? (a) only one kind of example (b) clear, sharp examples (c) wrong labels (d) very few examples
32. 32.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will be perfect (d) it will turn yellow
33. 33.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
34. 34.🔍 Which is the odd one out? (a) only one kind of example (b) blurry, unclear photos (c) very few examples (d) correct labels
35. 35.Which of these is good for training a model? (a) wrong labels (b) copies of one photo (c) very few examples (d) many varied examples
36. 36.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
37. 37.🏷️ What is a “label” in machine learning? (a) the font size (b) a sticker on a laptop (c) the price (d) the right answer tag
38. 38.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
39. 39.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
40. 40.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True

Free ai for students lessons, practice and worksheets at talentjr.in/ai-for-students