Difference between revisions of "User:ARuiz"
From REU@MU
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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.
