[slides prises du cours cs294-10 uc berkeley (2006 / 2009)]...
TRANSCRIPT
Regression
[slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]http://www.cs.berkeley.edu/~jordan/courses/294-fall09/lectures/regression/
Classification (reminder)
X ! YAnything:
• continuous (, d, …)
• discrete ({0,1}, {1,…k}, …)
• structured (tree, string, …)
• …
• discrete:
– {0,1} binary
– {1,…k} multi-class
– tree, etc. structured
Classification (reminder)
XAnything:
• continuous (, d, …)
• discrete ({0,1}, {1,…k}, …)
• structured (tree, string, …)
• …
Classification (reminder)
XAnything:
• continuous (, d, …)
• discrete ({0,1}, {1,…k}, …)
• structured (tree, string, …)
• …
Perceptron
Logistic Regression
Support Vector Machine
Decision TreeRandom Forest
Kernel trick
Regression
X ! Y• continuous:– , d
Anything:
• continuous (, d, …)
• discrete ({0,1}, {1,…k}, …)
• structured (tree, string, …)
• …
1
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.)
Occam’s razor principle:
Logiciels:◦ Weka (Java): http://www.cs.waikato.ac.nz/ml/weka/
◦ RapidMiner (nicer GUI?): http://rapid-i.com/
◦ SciKit Learn (Python): 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
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