No places left; one can still apply but applicants will be put on a waiting list.

Después de un anuncio sobre este evento a través de  estudiantes@cimat, ya llegaron tantas solicitudes que tuvimos que cerrar el registro muy pronto. Hay una lista de espera de 15 personas.

A short course on Big Data & Machine Learning by:

Prof. David J Lary
William B. Hanson Center for Space Science
University of Texas at Dallas, USA

A practical course based on Matlab for students and researchers in Computer Science, Statistics and Applied Mathematics:

Some of the topics that will be covered:
     Neural networks 
     Support vector machines 
     Decision trees
     Random forests 
     Self organizing maps




Experience with programming; basic knowledge of  MATLAB.


Deadline for registration: May, 31.

As the number of participants is very limited, we recommend to register as soon as possible (interested participants from CIMAT-DEMAT should register as well).

Registration fee:   $ 500 M.N.
No registration fee for people from the academic world

A limited number of rooms are available at CIMATEL, the guest house of CIMAT; 
cost of 3 nights with meals: $ 1.000 M.N. Students can apply for partial support .




Machine learning and multiple massive Big Data sets can be of great use for a wide variety of scientific, societal and business applications. The World Health Organization issued a report stating that seven million people died in 2012 from pollution related issues. Each year there are an estimated 219 million cases of Malaria. Eleven states have recently made drought declarations. Every year the US spends between $1 and $2 billion fighting fires. Issues such as these are of massive societal and personal relevance. Big Data and machine learning can provide invaluable tools for both improved understanding and making data driven decisions and policy.

This workshop will give an introduction to a wide range of Big Data applications of major scientific and societal importance such as environmental health, drought and water issues, fire.

The practical tools introduced can be readily used in a wide range of applications from research to real time decision support. The data used comes from a wide variety of sources including scientific instrumentation, social media, remote sensing, aerial vehicles and the internet of things.



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