• 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…

Trick 1: Nearest Neighbor

• Task : predict what houses are most likely to donate to an election

Nearest Neighbor

• Task : predict what houses are most likely to donate to an election

• Know some voter• registrations

Nearest Neighbor

• Task : predict what houses are most likely to donate to an election

• What should wepredict for the ?marks

Nearest Neighbor

• Task : predict what houses are most likely to donate to an election

• Should we considermore than oneneighbor?

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

Simple Nearest Neighbor

• Nearest Neighbor Applied

Pattern Nearest Neighbor Nearest 3 Neighbors

Other Nearest Neighbor

• Nearness as pixel difference:

Trick 2: Decision Trees

• Sequnce of choices to make a decision

Do I need an umbrella?

Spam Filter

• Is a web page "spam"?

Spam Filter

• Is a web page "spam"?

Spam Filter

• Is a web page "spam"?

How do we decide the questions???

Machine Learning

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

Learning a Decision Tree

• http://aispace.org/dTree/

Human Involvement

• Still need to determine possible questions, things to look at

Human Involvement

• Still need to determine possible questions, things to look at– What should we look at for these???

Trick 3: Neural Networks

• Biologically inspired computation

Neural Networks

• Biologically inspired computation

Neural Networks

• A simple "take umbrella" network:

Neural Networks

Sunglasses Network

• Image recognition network:

Sunglasses Network

• Image recognition network:

Enhanced Neurons

• Signals can be any value 0-1

Enhanced Neurons

• Signals can be any value 0-1• Inputs can be weighted

Enhanced Neurons

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

Learning

• http://aispace.org/neural/

Result

• One neuron's weights

Making it all worth it

• http://www.cs.cmu.edu/~tom7/mario/