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13-PracticalMachineLearning

13-PracticalMachineLearning - Prac╩l Machine Learning...

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Prac%cal’Machine’Learning’ Verena’Kaynig-Fi4kau’([email protected])’ We’are’drowning’in’informa%on’and’ starving’for’knowledge’ John’ Naisbi4’
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Machine’Learning’ Analyze’training’data’ Make’predic%ons’for’new’unseen’data:’ supervised’learning’ Find’pa4erns:’ unsupervised’learning’
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Machine’Learning’ Supervised’Learning’ SVM,’Decision’Tree,’Boos%ng,’Random’Forest’ Unsupervised’Learning’ K-means,’mean’shi±’
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Supervised’Learning’ x1’ x2’ data’points’ labels’ features’ separa%ng’ hyper’plane’
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Features’are’important’ weight’ roundness’ ?
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Features’are’important’ color’ shape’
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Google’s±Self-Driving±Car±
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Car’Features ’ Laser’scan’ Intensity’model’ Eleva%on’model’ Camera’vision’ 2D’sta%onary’map’ Lane’model’
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So’just’measure’everything?’ More’features’=’be4er’classiFca%on?’ Prac%cal’issues:’ Data’volume,’computa%on’overhead’ Theore%cal’issues:’ Generaliza%on’performance’ Curse’of’dimensionality’
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Supervised’Learning’ x1’ x2’ data’points’ labels’ features’ separa%ng’ hyper’plane’
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Perceptron’ x:’data’point’ y:’label’ w:’weight’vector’ b:’bias’ w’ -1 +1 x1’ x2’ x3’ -1’ b’ w3’ w2’ w1’
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The’XOR’Problem’ x1’ x2’ x3’
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Support’Vector’Machine’ Widely’used’for’all’sorts’of’classiFca%on’ problems’ www.clopinet.com/isabelle/Projects/SVM/applist.html Some’people’say’it’is’the’best’of’the’shelf’ classiFer’out’there’
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Maximum’Margin’Classifca%on’ x1’ x2’ x1’ x2’
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What’about’outliers?’ x2’ x1’ :’slack’variables’
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x=0’ XOR’problem’revised’ Did’we’add’informa%on’to’make’the’problem’ seperable?’
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Polynomial’Kernel’in’3D’
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Quadra%c’Kernel’
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Polynomial:’ Radial’basis’func%on’(RBF):’ Kernel’Func%ons’
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Kernel’Trick’for’SVMs’ Arbitrary’many’dimensions’ Li4le’computa%onal’cost’ Maximal’margin’helps’with’curse’of’ dimensionality’
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