Merge branch 'master' of https://github.com/snap-stanford/cs224w-notes

_config.yml

@@ -1,13 +1,12 @@
 baseurl: /cs224w-notes
-title: Course on machine learning with graphs
-subtitle: Lecture notes for Stanford CS 224W.
-author: Manan Shah
+title: Course on Machine Learning with Graphs
+subtitle: Lecture Notes for Stanford CS224W.
 simple_search: http://google.com/search
-description: LLecture notes for Stanford CS 224W.
+description: Lecture Notes for Stanford CS224W.
 name: cs224w-notes
 markdown_ext: "markdown,mkdown,mkdn,mkd,md"
 permalink: /articles/:short_year/:title
-timezone: America/New_York
+timezone: America/Los_Angeles
 excerpt_separator: <!--more-->  # you can specify your own separator, of course.
 exclude: ['Gemfile', 'Gemfile.lock', 'Rakefile', 'README.md']
 post:

index.md

@@ -5,7 +5,7 @@ title: Contents
 <span class="newthought">These notes</span> form a concise introductory course on machine learning with large-scale graphs. They mirror the topics topics covered by Stanford [CS224W](https://cs224w.stanford.edu), and are written by the CS 224W TAs. 
 {% include marginnote.html id='mn-construction' note='The notes are still under construction! They will be written up as lectures continue to progress. If you find any typos, please let us know, or submit a pull request with your fixes to our [GitHub repository](https://github.com/snap-stanford/cs224w-notes).'%}
 
-You too may help make these notes better by submitting your improvements to us via [GitHub](https://github.com/ermongroup/cs228-notes). Note that submitting substantial improvements will result in *bonus points* being added to your overall grade!
+You too may help make these notes better by submitting your improvements to us via [GitHub](https://github.com/snap-stanford/cs224w-notes). Note that submitting substantial improvements will result in *bonus points* being added to your overall grade!
 
 Starting with the Fall 2019 offering of CS 224W, the course covers three broad topic areas for understanding and effectively learning representations from large-scale networks: preliminaries, network methods, and machine learning with networks. Subtopics within each area correspond to individual lecture topics.