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Artificial Intelligence: Advanced Search and Machine Learning Lecture 15 ,[object Object],[object Object],[object Object]
Decision Tree Induction ,[object Object],[object Object],[object Object]
Decision Tree Stripy Long Tail Big Teeth Big Teeth yes no no no no yes yes yes Lion Rabbit Lemur Tiger zebra Traverse tree based on values of features. Leaf node gives classification. (equivalent to logical formulae that may contain disjunctions, but more natural representation, easier for humans to use)
Decision Tree Induction ,[object Object],[object Object],[object Object]
A sample data set (with extra tigers)
Creating the Decision Tree ,[object Object],[object Object],[object Object],[object Object]
Tiger Tree Big Teeth? yes no {1, 3, 4, 5} {2, 6} “ Yes” branch has 3 tigers, 1 non tiger. “ No” branch just has non tigers. .. So we need to do further work on yes branch, but “no” branch can have label “not tiger”
Tiger Tree ,[object Object],[object Object],Big Teeth? yes no Not a tiger Stripy Tiger Not a tiger
In general.. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary: Decision Tree Induction ,[object Object],[object Object],[object Object]
Neural Networks ,[object Object],[object Object],Soma Axon Dendrites Axon Synapse
Biological Neuron ,[object Object],[object Object],[object Object],[object Object]
Perceptron ,[object Object],[object Object],[object Object],ouput inputs w1 w2 w3 w4 x1 x2 x3 x4
Perceptron ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learning in Neural Nets ,[object Object],[object Object],[object Object],[object Object],[object Object]
Learning in NNs ,[object Object],[object Object],[object Object],[object Object],[object Object],ouput 0.5 0.5 0.5 1 1 1 1 OK, don’t change .
Learning tigers ,[object Object],[object Object],output 0.5 0.5 0.5 1 1 0 0 output 0.5 0.6 0.6 1 1 1 0
Learning tigers ,[object Object],[object Object],[object Object],output 0.4 0.5 0.6 1 1 0 0
Perceptron Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
NN: Summary ,[object Object],[object Object],[object Object],[object Object]
Evaluating Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object]

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WEB DESIGN!
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l15.ppt

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  • 3. Decision Tree Stripy Long Tail Big Teeth Big Teeth yes no no no no yes yes yes Lion Rabbit Lemur Tiger zebra Traverse tree based on values of features. Leaf node gives classification. (equivalent to logical formulae that may contain disjunctions, but more natural representation, easier for humans to use)
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  • 5. A sample data set (with extra tigers)
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  • 7. Tiger Tree Big Teeth? yes no {1, 3, 4, 5} {2, 6} “ Yes” branch has 3 tigers, 1 non tiger. “ No” branch just has non tigers. .. So we need to do further work on yes branch, but “no” branch can have label “not tiger”
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