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[slides prises du cours cs294-10 UC Berkeley (2006 / 2009)] http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/regression/

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Classification (reminder) X ! Y Anything: continuous ( , d, …) discrete ({0,1}, {1,…k}, …) structured (tree, string, …) … discrete: – {0,1}binary – {1,…k}multi-class – tree, etc.structured

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Classification (reminder) X Anything: continuous ( , d, …) discrete ({0,1}, {1,…k}, …) structured (tree, string, …) …

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Classification (reminder) X Anything: continuous ( , d, …) discrete ({0,1}, {1,…k}, …) structured (tree, string, …) … Perceptron Logistic Regression Support Vector Machine Decision Tree Random Forest Kernel trick

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Regression X ! Y continuous: – , d Anything: continuous ( , d, …) discrete ({0,1}, {1,…k}, …) structured (tree, string, …) … 1

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degree 15 overfitting!

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Between two models / hypotheses which explain as well the data, choose the simplest one In Machine Learning: ◦ we usually need to tradeoff between training error model complexity ◦ can be formalized precisely in statistics (bias- variance tradeoff, etc.)

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training errormodel complexity

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Logiciels: ◦ Weka (Java): http://www.cs.waikato.ac.nz/ml/weka/ http://www.cs.waikato.ac.nz/ml/weka/ ◦ RapidMiner (nicer GUI?): http://rapid-i.com/ http://rapid-i.com/ ◦ SciKit Learn (Python): http://scikit-learn.org http://scikit-learn.org Livres: ◦ Pattern Classification (Duda, Hart & Stork) ◦ Pattern Recognition and Machine Learning (Bishop) ◦ Data Mining (Witten, Frank & Hall) ◦ The Elements of Statistical Learning (Hastie, Tibshirani, Friedman) Programmer en python: ◦ cours cs188 de Dan Klein à Berkeley: http://inst.eecs.berkeley.edu/~cs188/fa10/lectures.html http://inst.eecs.berkeley.edu/~cs188/fa10/lectures.html

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Kernel Regression 02468101214161820 -10 -5 0 5 10 15 Kernel regression (sigma=1)

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