Difference between revisions of "User:ARuiz"
From REU@MU
| Line 18: | Line 18: | ||
* Attended ethics training | * Attended ethics training | ||
* Started learning about machine learning by implementing simple multivariate linear regression algorithms. | * 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: | Week 4: | ||
| Line 36: | Line 36: | ||
* Started learning about the implementation of neural networks. | * 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 |
Revision as of 18:17, 12 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
