Libardo1 · April 4, 2017 13:51
diff --git a/markdown b/markdown
 # Quick List of Resources for Topological Data Analysis

 ## Survey Papers
 Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject
 * [Topology and Data](http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01249-X/S0273-0979-09-01249-X.pdf) by Gunnar Carlsson
 * [Barcodes: The Persistent Topology of Data](https://www.math.upenn.edu/~ghrist/preprints/barcodes.pdf)

 ## Other Papers and Web Resources
 * [Extracting insights from the shape of complex data using topology](http://www.nature.com/articles/srep01236) A good introductory paper in Nature on the `Mapper` algorithm.
 * [Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition](https://research.math.osu.edu/tgda/mapperPBG.pdf) A more technical presentation of `Mapper`.
 * [Topological Data Analysis and Machine Learning Theory](https://www.birs.ca/workshops/2012/12w5081/report12w5081.pdf) Applications of TDA to machine learning.
 * Ayasdi, the company founded by Gurjeet Singh and Gunnar Carlsson, have several good [videos and whitepapers](http://www.ayasdi.com/resources) on how they use `Mapper` and TDA in machine learning pipelines.

 ## Software

 ## Books
 * [Computational Topology: An Introduction](http://www.amazon.com/Computational-Topology-Introduction-Herbert-Edelsbrunner/dp/0821849255) A good introducgtory book on persistent homology
 * [Elementary Applied Topology](https://www.math.upenn.edu/~ghrist/notes.html) A book by Robert Ghrist that goes beyond applications of algebraic toplogy to data analysis, but a very good read.  There is a very inexpensive print version and the PDF is available for free.
	# Quick List of Resources for Topological Data Analysis

	## Survey Papers
	Both Carlsson's and Ghrist's survey papers offer a very good introduction to the subject
	* [Topology and Data](http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01249-X/S0273-0979-09-01249-X.pdf) by Gunnar Carlsson
	* [Barcodes: The Persistent Topology of Data](https://www.math.upenn.edu/~ghrist/preprints/barcodes.pdf)

	## Other Papers and Web Resources
	* [Extracting insights from the shape of complex data using topology](http://www.nature.com/articles/srep01236) A good introductory paper in Nature on the `Mapper` algorithm.
	* [Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition](https://research.math.osu.edu/tgda/mapperPBG.pdf) A more technical presentation of `Mapper`.
	* [Topological Data Analysis and Machine Learning Theory](https://www.birs.ca/workshops/2012/12w5081/report12w5081.pdf) Applications of TDA to machine learning.
	* Ayasdi, the company founded by Gurjeet Singh and Gunnar Carlsson, have several good [videos and whitepapers](http://www.ayasdi.com/resources) on how they use `Mapper` and TDA in machine learning pipelines.

	## Software

	## Books
	* [Computational Topology: An Introduction](http://www.amazon.com/Computational-Topology-Introduction-Herbert-Edelsbrunner/dp/0821849255) A good introducgtory book on persistent homology
	* [Elementary Applied Topology](https://www.math.upenn.edu/~ghrist/notes.html) A book by Robert Ghrist that goes beyond applications of algebraic toplogy to data analysis, but a very good read. There is a very inexpensive print version and the PDF is available for free.