Difference between revisions of "User:ARuiz"
From REU@MU
| Line 15: | Line 15: | ||
Week 4: | 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: | Week 5: | ||
* learned about regularization for some machine learning models. | * learned about regularization for some machine learning models. | ||
| − | |||
* Read uncertainty quantification papers. | * Read uncertainty quantification papers. | ||
| − | + | * Read papers on probabilistic forecasting. | |
| − | * Read papers on probabilistic forecasting | + | * Prepared and practiced presentation for REU talk. |
Week 6: | Week 6: | ||
* Started learning about the implementation of neural networks. | * Started learning about the implementation of neural networks. | ||
| − | |||
* Read uncertainty quantification papers | * Read uncertainty quantification papers | ||
| − | |||
* Finally finished reading Gneiting et al. (2014) paper on probabilistic forecasting. | * Finally finished reading Gneiting et al. (2014) paper on probabilistic forecasting. | ||
| − | |||
* Interviewed engineers for insight on uncertainty quantification. | * Interviewed engineers for insight on uncertainty quantification. | ||
Revision as of 00:13, 9 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 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.
- Read uncertainty quantification papers
- Finally finished reading Gneiting et al. (2014) paper on probabilistic forecasting.
- Interviewed engineers for insight on uncertainty quantification.
