how do you get here? https:// youtube/watch?v=t9fxp3hk6di
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
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• How do you get here?https://www.youtube.com/watch?v=t9Fxp3HK6DI
![Page 2: How do you get here? https:// youtube/watch?v=t9Fxp3HK6DI](https://reader035.vdocuments.us/reader035/viewer/2022062721/568135f4550346895d9d66ca/html5/thumbnails/2.jpg)
Pattern Recognition& Machine Learning
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Patterns
• Humans are excellent at recognizing patterns
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Patterns
• Even if we can't explain how we do it…
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Trick 1: Nearest Neighbor
• Task : predict what houses are most likely to donate to an election
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Nearest Neighbor
• Task : predict what houses are most likely to donate to an election
• Know some voter• registrations
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Nearest Neighbor
• Task : predict what houses are most likely to donate to an election
• What should wepredict for the ?marks
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Nearest Neighbor
• Task : predict what houses are most likely to donate to an election
• Should we considermore than oneneighbor?
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Simulator:• http://www.cs.cmu.edu/~zhuxj/courseproject/knndemo/KNN.html
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Simple Nearest Neighbor
• Nearest Neighbor Applied
Pattern Nearest Neighbor Nearest 3 Neighbors
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Other Nearest Neighbor
• Nearness as pixel difference:
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Trick 2: Decision Trees
• Sequnce of choices to make a decision
Do I need an umbrella?
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Spam Filter
• Is a web page "spam"?
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Spam Filter
• Is a web page "spam"?
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Spam Filter
• Is a web page "spam"?
How do we decide the questions???
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Machine Learning
• Machine Learning : Build a general algorithm to LEARN specific patterns
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Human Involvement
• Still need to determine possible questions, things to look at
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Human Involvement
• Still need to determine possible questions, things to look at– What should we look at for these???
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Trick 3: Neural Networks
• Biologically inspired computation
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Neural Networks
• Biologically inspired computation
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Neural Networks
• A simple "take umbrella" network:
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Neural Networks
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Sunglasses Network
• Image recognition network:
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Sunglasses Network
• Image recognition network:
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Enhanced Neurons
• Signals can be any value 0-1
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Enhanced Neurons
• Signals can be any value 0-1• Inputs can be weighted
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Enhanced Neurons
• Signals can be any value 0-1• Inputs can be weighted• Threshold function is not all or nothing– Produces values 0-1
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Result
• One neuron's weights
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Making it all worth it
• http://www.cs.cmu.edu/~tom7/mario/