Difference between revisions of "Machine Learning"

From Federation of Earth Science Information Partners
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=== Get Involved===
=== Get Involved===
* '''Email List:''' [https://lists.esipfed.org/mailman/listinfo/esip-machinelearning https://lists.esipfed.org/mailman/listinfo/esip-machinelearning]
* '''Email List''', [https://lists.esipfed.org/mailman/listinfo/esip-machinelearning https://lists.esipfed.org/mailman/listinfo/esip-machinelearning]
* Next meeting:
* '''Upcoming meetings'''
** Telecon: Friday, 9/28, 9:00PT/10:00MT/11:00CT/12:00ET [https://global.gotomeeting.com/join/335719413  GoToMeeting] | Access Code: 335-719-413
** Telecon: Friday, 11/16, 9:00PT/10:00MT/11:00CT/12:00ET, Access Code: 401-356-069
** Dial In: United States: +1 (872) 240-3311  Access Code: 335-719-413
*** [https://global.gotomeeting.com/join/401356069  GoToMeeting]
*** Dial In: United States: +1 (646) 749-3112
** Telecon: Friday, 12/21, 9:00PT/10:00MT/11:00CT/12:00ET, Access Code: 933-291-469
*** [https://global.gotomeeting.com/join/933291469    GoToMeeting]
*** Dial In: United States: +1 (646) 749-3122
* '''Contact Chairs:'''  
* '''Contact Chairs'''  
**Anne Wilson, Laboratory for Atmospheric and Space Physics (LASP)
**Anne Wilson, Laboratory for Atmospheric and Space Physics (LASP)
**Beth Huffer, Lingua Logica
**Beth Huffer, Lingua Logica
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Revision as of 09:17, October 30, 2018

Machine learning, everybody’s doing it. Scientists are applying machine learning to their scientific algorithms and using the results to justify various conclusions. Is machine learning the silver bullet that will help us answer our scientific questions?

The purpose of this cluster is to educate ourselves and the ESIP community about machine learning through asking and answering questions and sharing experiences and resources. The scope of the cluster includes topics like the following:

  • What is machine learning? What can machine learning do? How is machine learning different from data science? From data analytics?
  • What types of machine learning algorithms are there and how do they compare to each other? Under what circumstances is each best applied and/or not applicable?
  • What are symbolic and subsymbolic approaches to machine learning and their subtypes?
  • What are machine learning SWOTs: strengths, weaknesses, opportunities, threats? Some considerations include:
    • models, model choices and biases
    • p-hacking, data dredging
  • What machine learning tools are available?
  • In particular, regarding deep learning/neural networks
    • How do supervised, semi-supervised, and unsupervised networks differ?
    • How to integrate truth labeled data?
    • Under what circumstances is it okay to not understand what the network learned? When is it not okay?
    • How to deal with a lack of training data?
    • How to (try to) understand what was learned?

Possible cluster outputs could include:

  • A machine learning tool survey and SWOT analysis
  • Training material, on line [and maybe a guide to available platforms and resources? e.g., using AWS ML platform]
  • Training and/or other sessions at ESIP
  • Recommendations regarding the application of ML in earth and space sciences




Get Involved

  • Contact Chairs
    • Anne Wilson, Laboratory for Atmospheric and Space Physics (LASP)
    • Beth Huffer, Lingua Logica
    • Arif Albayrak, Goddard
    • Hook Hua, JPL



  1. Hidden Technical Debt in Machine Learning Systems, File:NIPS-5656-hidden-technical-debt-in-machine-learning-systems.pdf.