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@@ -532,6 +532,11 @@ Courses
 	- [Labs and Assignments](http://cs109.github.io/2014/pages/homework.html)
 	- [2014 Lectures](http://cs109.github.io/2014/)
 	- [2013 Lectures](http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml) *(slightly better)*
+- [CS 156](https://work.caltech.edu/telecourse.html) **Learning from Data** *Caltech* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20    " height="20" alt="Lecture Notes" title="Lecture Notes" />
+	- This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.  
+	- [Lectures](https://work.caltech.edu/lectures.html)
+	- [Homework](https://work.caltech.edu/homeworks.html)
+	- [Textbook](https://work.caltech.edu/textbook.html)
 - [CS 188](http://ai.berkeley.edu/home.html) **Introduction to Artificial Intelligence** *UC Berkeley* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20    " height="20" alt="Lecture Notes" title="Lecture Notes" />
     - This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
     - [Lectures](http://ai.berkeley.edu/lecture_videos.html)