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Edited authorship attribution to reflect previous commit's standard

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1 parent 2a6129a commit 3ac4f762fbe4dcb0cbf1a852fcab5762cbeaf78c @drewconway drewconway committed Feb 12, 2012
@@ -1,6 +1,6 @@
# File-Name: package_installer.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.conway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.conway@nyu.edu)
# Purpose: Install all of the packages needed for the Machine Learning for Hackers case studies
# Data Used: n/a
# Packages Used: n/a
@@ -1,6 +1,6 @@
# File-Name: ufo_sightings.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.conway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.conway@nyu.edu)
# Purpose: Code for Chapter 1. In this case we will review some of the basic
# R functions and coding paradigms we will use throughout this book.
# This includes loading, viewing, and cleaning raw data; as well as
@@ -1,6 +1,6 @@
# File-Name: email_classify.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.conway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.conway@nyu.edu)
# Purpose: Code for Chapter 3. In this case we introduce the notion of binary classification.
# In machine learning this is a method for determining what of two categories a
# given observation belongs to. To show this, we will create a simple naive Bayes
@@ -1,6 +1,6 @@
# File-Name: priority_inbox.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.conway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.conway@nyu.edu)
# Purpose: Code for Chapter 4. In this case study we will attempt to write a "priority
# inbox" algorithm for ranking email by some measures of importance. We will
# define these measures based on a set of email features, which moves beyond
View
@@ -1,6 +1,6 @@
# File-Name: senate_mds.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.conway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.conway@nyu.edu)
# Purpose: Code for Chapter 4. In this case study we introduce multidimensional scaling (MDS),
# a technique for visually displaying the simialrity of observations in
# mutlidimensional space. We begin with with a very simple example using simulated
@@ -1,6 +1,6 @@
# File-Name: google_sg.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.conway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.conway@nyu.edu)
# Purpose: File 1 for code from Chapter 11. This file contains a set of functions for building
# igraph network object from the Twitter social graphs. As the initial set of code
# used in this case, we will write functions that query the Google SocialGraph API
@@ -1,6 +1,6 @@
# File-Name: twitter_net.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.cownway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.cownway@nyu.edu)
# Purpose: File 2 for code in Chapter 11. In this short file we write code for generating the
# the ego-network for a given Twitter user. Once the network object has been built we
# add some vertex level attributs, and clean the graph by extracting the 2-corre. Finally,
@@ -1,6 +1,6 @@
# File-Name: twitter_rec.R
# Date: 2012-02-10
-# Author: Drew Conway (drew.conway@nyu.edu) and John Myles White (jmw@johnmyleswhite.com)
+# Author: Drew Conway (drew.conway@nyu.edu)
# Purpose: File 3 for code in Chapter 9. In the final piece of this case study we design a
# simple social graph reccommendation system based on Twitter data. Using the
# data generated in the previous files, we can make recommendations for users

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