MJ
Milan Joshi
@mlnjsh
Topological Data Analysis Theory and Practice
Submitted Aug 17, 2017
Section:
Full talk
Technical level:
Intermediate
As we are already living in the age of big data and it is too big to ignore. Therefore it is important that we find ways to explore, summarize , and answer questions with this data. However the problem is not just that the data is big, but that it is complicated, loaded with surprising patterns, unusual structures, Often that means it is even too complicated for the standard methods to be useful . In this Talk I will discuss a new collection of tools available from the field known collectively as Topological data analysis(TDA). TDA is relatively new branch of Mathematics , it’s an approach to extract shapes(patterns) in data and obtain insights from datasets using techniques from topology, Topology is very old branch of pure Mathematics. I will discuss about the technology called Persistent homology , Barcodes, Persistent Landscape in TDA .Finally we also discuss the scope and future of this field with few applications and some tools and software’s in which TDA can be done .
Outline
What is Topology
what is topological data analysis
What is Persistent Homology
What is Mapper Algorithm
How it helps in solve complex problems
How it differs from traditional machine Learning
How to do TDA in R
Some Applications
Requirements
laptops and R software installed
{{ gettext('Login to leave a comment') }}
{{ gettext('Post a comment…') }}{{ errorMsg }}
{{ gettext('No comments posted yet') }}