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+# Intro
+
+* Predictive or Supervised: learn a mappping from inputs x to outputs u, given a labeled set of input-output paris (the training set).
+	- The training input x_i is called features, attributes, covariates.
+	- If y_i assumes a value from a finite set, it's called categorical or nominal, and the problem is classification or pattern recognition. If y_i us real-valued scalar, it is regression.
+
+* Descriptive or unsupervised learning: find patterns in the data (knowledge discovery). c
+
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+# Intro
+
+* Predictive or Supervised: learn a mappping from inputs x to outputs u, given a labeled set of input-output paris (the training set).
+	- The training input x_i is called features, attributes, covariates.
+	- If y_i assumes a value from a finite set, it's called categorical or nominal, and the problem is classification or pattern recognition. If y_i us real-valued scalar, it is regression.
+
+* Descriptive or unsupervised learning: find patterns in the data (knowledge discovery). c
+
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+++ b/README.md
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 ## Kaggles
 
+
 ## Papers