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How do you get here? https:// youtube/watch?v=t9Fxp3HK6DI

How do you get here? https:// www.youtube.com/watch?v=t9Fxp3HK6DI. Pattern Recognition & Machine Learning. Patterns. Humans are excellent at recognizing patterns. Patterns. Even if we can't explain how we do it…. Nearest Neighbor.

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How do you get here? https:// youtube/watch?v=t9Fxp3HK6DI

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  1. How do you get here?https://www.youtube.com/watch?v=t9Fxp3HK6DI

  2. Pattern Recognition& Machine Learning

  3. Patterns • Humans are excellent at recognizing patterns

  4. Patterns • Even if we can't explain how we do it…

  5. Nearest Neighbor • Task : predict what houses are most likely to donate to an election

  6. Nearest Neighbor • Task : predict what houses are most likely to donate to an election • Know some voter • registrations

  7. Nearest Neighbor • Task : predict what houses are most likely to donate to an election • What should wepredict for the ?marks

  8. Nearest Neighbor • Task : predict what houses are most likely to donate to an election • Should we considermore than oneneighbor?

  9. Simulator: • http://www.cs.cmu.edu/~zhuxj/courseproject/knndemo/KNN.html

  10. Simple Nearest Neighbor • Nearest Neighbor Applied Pattern Nearest Neighbor Nearest 3 Neighbors

  11. Other Nearest Neighbor • Nearness as pixel difference:

  12. Decision Trees: • Sequnce of choices to make a decision Do I need an umbrella?

  13. Spam Filter • Is a web page "spam"?

  14. Spam Filter • Is a web page "spam"?

  15. Spam Filter • Is a web page "spam"? How do we decide the questions???

  16. Machine Learning • Machine Learning : Build a general algorithm to LEARN specific patterns

  17. Learning a Decision Tree • http://aispace.org/dTree/

  18. Human Involvement • Still need to determine possible questions, things to look at

  19. Human Involvement • Still need to determine possible questions, things to look at • What should we look at for these???

  20. Neural Networks • Biologically inspired computation

  21. Neural Networks • Biologically inspired computation

  22. Neural Networks • A simple "take umbrella" network:

  23. Neural Networks

  24. Sunglasses Network • Image recognition network:

  25. Sunglasses Network • Image recognition network:

  26. Enhanced Neurons • Signals can be any value 0-1

  27. Enhanced Neurons • Signals can be any value 0-1 • Inputs can be weighted

  28. Enhanced Neurons • Signals can be any value 0-1 • Inputs can be weighted • Threshold function is not all or nothing • Produces values 0-1

  29. Learning • http://aispace.org/neural/

  30. Result • One neuron's weights

  31. Making it all worth it • http://www.cs.cmu.edu/~tom7/mario/

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