Move notes to evernote

Notes/ML_A_Probabilistic_Perspective_Murphy.md

@@ -1,8 +0,0 @@
-# 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
-

Papers/Adversarial_examples_1607.02533v1.md

@@ -1,12 +0,0 @@
-# Adversarial Examples in the Physical World
-
-## Kurakin, Goodfellow, Bengio
-http://arxiv.org/pdf/1607.02533v1.pdf
-
-* An adversarial example is a sample of input data which has been modified
-very slightly in a way that is intended to cause a machine learning classifier
-to misclassify it.
-
-* Adversarial examples pose security concerns because they could be
-used to perform an attack on machine learning systems, even if the adversary has
-no access to the underlying model