Data Science Resources: Stellar Classification using Machine Learning

02/06/16 Tech Talk: Stellar Classification using Machine Learning – by Sandra Greiss

As part of our series of Cambridge Coding Academy Tech Talks, this presentation explores Machine Learning applications in astrophysics.


In this talk, I use the spectra (stellar flux vs wavelength) of identified stars to build a classifier which will detect the identity of the stars. Using a very simple non linear SVM, I achieve an 86% accuracy with my model. The next step is to use deep neural nets to achieve a better accuracy.


Sandra Greiss presenting at PyData London 2016


If you are interested in Machine Learning and Deep Learning? Learn more at our Data Science Bootcamp: https://cambridgecoding.com/datascience-bootcamp 


Sandra Greiss

I joined Lyst in December 2014, as a junior data scientist, a couple of months after I finished my PhD in Astronomy and Astrophysics (Warwick). Lyst is an online fashion website, where we aggregate millions of products from thousands of retailers. I am responsible for building the ‘autocomplete’ and ‘did you mean’ features of Search on our website. I have now moved towards more data science projects, where I want to predict the fabric of clothes, shoes and bags on Lyst using their descriptions and images. I use crowdsourcing to tag the products for the training dataset of my models. Besides work, I have a weird obsession with shoes, good food and travelling every few weeks! http://www.lyst.com

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