Difference between revisions of "User:ARuiz"

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* Read some of the scientific literature on uncertainty quantification and probabilistic forecasting.
 
* Read some of the scientific literature on uncertainty quantification and probabilistic forecasting.
 
* Met with Dr. Corliss
 
* Met with Dr. Corliss
 +
 +
Week 7:
 +
 +
* Interviewed Dr. Lin on uncertainty quantification.
 +
* Met with Mohammad Saber to discuss probabilistic forecasting and our work.
 +
* Finished writing some of the sections for the REU report.
 +
* Met with Dr. Corliss.
 +
* Attended poster presentation talk for the REU lunch.
 +
* Read some sections of  Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American Statistical Association102.477 (2007): 359-378.

Revision as of 20:06, 19 July 2016

Week 1:

  • Read Superforecasting: The art and science of prediction. Random House, 2015 by Tetlock, Philip, and Dan Gardner.
  • Met with mentor for background on the GasDay Labs mission and our research topic.
  • Met with mentor for background on the specifics in GasDay Labs models.
  • Started researching the scientific literature for examples of uses of probabilistic forecasting.
  • Worked with the Brier score metric in order to identify potential flaws.

Week 2:

  • Further reading into applications for probabilistic forecasting.
  • Implemented basic probabilistic forecasting scoring metrics (pinball, Winkler, Brier).
  • Read papers on probabilistic load forecasting.
  • Familiarized myself with GasDay Labs' database.

Week 3:

  • Attended ethics training
  • Started learning about machine learning by implementing simple multivariate linear regression algorithms.
  • Read some of the scientific literature for scoring metrics and probabilistic forecast properties.

Week 4:

  • Learned about categorical classification machine learning algorithms.
  • Read papers about about different strengths and weaknesses in scoring metrics.
  • Prepared presentation for GasDay talk.

Week 5:

  • learned about regularization for some machine learning models.
  • Read uncertainty quantification papers.
  • Read papers on probabilistic forecasting.
  • Prepared and practiced presentation for REU talk.

Week 6:

  • Started learning about the implementation of neural networks.
  • Interviewed Matt Thomas for insight on uncertainty quantification.
  • Read some of the scientific literature on uncertainty quantification and probabilistic forecasting.
  • Met with Dr. Corliss

Week 7:

  • Interviewed Dr. Lin on uncertainty quantification.
  • Met with Mohammad Saber to discuss probabilistic forecasting and our work.
  • Finished writing some of the sections for the REU report.
  • Met with Dr. Corliss.
  • Attended poster presentation talk for the REU lunch.
  • Read some sections of Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American Statistical Association102.477 (2007): 359-378.