Finished ML. fucked up in desc

data.json

@@ -5,7 +5,7 @@
     "code": "CS 61C",
     "link": "http://www-inst.eecs.berkeley.edu/~cs61c/sp15/",
     "university": "UC Berkeley",
-    "category": "",
+    "category": "systems",
     "assignments": null,
     "readings": "http://www-inst.eecs.berkeley.edu/~cs61c/sp15/#Resources",
     "videos": "https://www.youtube.com/playlist?list=PL-XXv-cvA_iCl2-D-FS5mk0jFF6cYSJs_",
@@ -394,5 +394,94 @@
     "readings": "http://web.stanford.edu/~boyd/cvxbook/",
     "videos": null,
     "notes": "https://class.stanford.edu/courses/Engineering/CVX101/Winter2014/courseware/7206c57866504e83821d00b5d3f80793/"
+  },
+  {
+    "id": 34,
+    "title": "Deep Learning",
+    "code": "11-785",
+    "link": "http://deeplearning.cs.cmu.edu/",
+    "university": "Carnegie Mellon",
+    "category": "machine learning",
+    "assignments": "http://deeplearning.cs.cmu.edu/",
+    "readings": "http://deeplearning.cs.cmu.edu/",
+    "videos": null,
+    "notes": "http://deeplearning.cs.cmu.edu/"
+  },
+  {
+    "id": 35,
+    "title": "Machine Learning",
+    "code": "15-781",
+    "link": "http://www.cs.cmu.edu/~tom/10701_sp11/",
+    "university": "Carnegie Mellon",
+    "category": "machine learning",
+    "assignments": "http://www.cs.cmu.edu/~tom/10701_sp11/hws.shtml",
+    "readings": "http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml",
+    "videos": "http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml",
+    "notes": "http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml"
+  },
+  {
+    "id": 36,
+    "title": "Introduction to Matrix Methods",
+    "code": "EE 103",
+    "link": "http://stanford.edu/class/ee103/",
+    "description": "The course covers the basics of matrices and vectors, solving linear equations, least-squares methods, and many applications. It'll cover the mathematics, but the focus will be on using matrix methods in applications such as tomography, image processing, data fitting, time series prediction, finance, and many others. EE103 is based on a book that [Stephen Boyd](http://stanford.edu/~boyd/) and [Lieven Vandenberghe](http://www.seas.ucla.edu/~vandenbe/) are currently writing. Students will use a new language called [Julia](http://julialang.org/) to do computations with matrices and vectors.",
+    "university": "Stanford University",
+    "category": "machine learning",
+    "assignments": "http://stanford.edu/class/ee103/homework.html",
+    "readings": "http://stanford.edu/class/ee103/mma.html",
+    "videos": null,
+    "notes": "http://stanford.edu/class/ee103/lectures.html"
+  },
+  {
+    "id": 37,
+    "title": "Data Science",
+    "code": "CS 109",
+    "link": "http://cs109.github.io/2014/",
+    "description": "Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization, stories, and interpretable summaries.",
+    "university": "Harvard University",
+    "category": "machine learning",
+    "assignments": "http://cs109.github.io/2014/pages/homework.html",
+    "readings": null,
+    "videos": "http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml",
+    "notes": "http://cs109.github.io/2014/pages/schedule.html"
+  },
+  {
+    "id": 38,
+    "title": "Convolutional Neural Networks for Visual Recognition",
+    "code": "CS 231n",
+    "link": "http://cs231n.stanford.edu/",
+    "description": "Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.",
+    "university": "Stanford University",
+    "category": "machine learning",
+    "assignments": "http://cs231n.github.io",
+    "readings": null,
+    "videos": null,
+    "notes": "http://cs231n.stanford.edu/syllabus.html"
+  },
+  {
+    "id": 39,
+    "title": "Statistical and Discrete Methods for Scientific Computing",
+    "code": "CS395T",
+    "link": "http://www.nr.com/CS395T/",
+    "description": "Practical course in applying modern statistical techniques to real data, particularly bioinformatic data and large data sets. The emphasis is on efficient computation and concise coding, mostly in MATLAB and C++.",
+    "university": "University of Texas",
+    "category": "machine learning",
+    "assignments": "http://granite.ices.utexas.edu/coursewiki/index.php/Main_Page",
+    "readings": null,
+    "videos": null,
+    "notes": "http://granite.ices.utexas.edu/coursewiki/index.php/Main_Page"
+  },
+  {
+    "id": 40,
+    "title": "Machine Learning",
+    "code": "CS 4771",
+    "link": "http://www.cs.columbia.edu/~jebara/4771/",
+    "description": "Machine learning course taught by Tony Jebara",
+    "university": "Columbia University",
+    "category": "machine learning",
+    "assignments": "http://www.cs.columbia.edu/~jebara/4771/handouts.html",
+    "readings": null,
+    "videos": null,
+    "notes": "http://www.cs.columbia.edu/~jebara/4771/handouts.html"
   }
 ]

js/awesomecourses.js

@@ -38,6 +38,7 @@ var Form = React.createClass({
       title: course.title,
       code: course.code,
       link: course.link,
+      description: course.description,
       university: course.university,
       category: course.category,
       assignments: course.assignments || null,