Machine Learning

From Federation of Earth Science Information Partners
(Difference between revisions)
Jump to: navigation, search
Line 25: Line 25:
 
Possible cluster outputs could include:
 
Possible cluster outputs could include:
 
* A machine learning tool survey and SWOT analysis
 
* A machine learning tool survey and SWOT analysis
* Training material, on line
+
* 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
 
* Training and/or other sessions at ESIP
 
* Recommendations regarding the application of ML in earth and space sciences  
 
* Recommendations regarding the application of ML in earth and space sciences  

Revision as of 12:50, 17 August 2018

DRAFT, under construction

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 all our scientific questions? Can we all go home now?

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 questions 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
  • 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


News


Archive

Activities

Get Involved

  • Email List: List_Name_Here
  • Next meeting:
    • Telecon: WebEx | Password: XXX XXX XX
    • Dial In: 1-877-668-4493 Access Code: XXX XXX XX
  • Contact Chair:
    • Contact_Name_Here

Resources

Personal tools
Namespaces

Variants
Actions
Navigation
Toolbox