[R-SIG-Finance] New Webinar Series: The Evolution of Regression From Classical Linear Regression to Modern Ensembles

[Dirk Eddelbuettel]

On 7 February 2013 at 15:31, G See wrote:
| Lisa,
| 
| Please don't crosspost.  R-help was was enough.  Please don't abuse the lists.

Seconded, in my role as listmaster for r-sig-finance and r-sig-hpc. 

Please respect list etiquette or else I may be forced to unsubscribe you.

Dirk
 
| Thank you,
| Garrett
| 
| On Thu, Feb 7, 2013 at 2:58 PM, Lisa Solomon <lisasolomon at alum.mit.edu> wrote:
| > The Evolution of Regression: An Upcoming Webinar Series
| > (Hands-on Component)
| >
| > Registration:
| > http://bit.ly/salford-systems-regression-webinar-series
| >
| > Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series addresses these problems. Are you working with larger datasets? Is your data challenging? Does your data include missing values, nonlinear relationships, local patterns and interactions? This webinar series is for you! We will cover improvements to conventional and logistic regression, and will include a discussion of classical, regularized, and nonlinear regression, as well as modern ensemble and data mining approaches. This series will be of value to any classically trained statistician or modeler.
| >
| > Overcoming Linear Regression Limitations
| >
| > Part 1: March 1 - Regression methods discussed
| > •       Classical Regression
| > •       Logistic Regression
| > •       Regularized Regression: GPS Generalized Path Seeker
| > •       Nonlinear Regression: MARS Regression Splines
| >
| > Part 2: March 15 - Hands-on demonstration of concepts discussed in Part 1
| > •       Step-by-step demonstration
| > •       Datasets and software available for download
| > •       Instructions for reproducing demo at your leisure
| > •       For the dedicated student: apply these methods to your own data (optional)
| >
| > Part 3: March 29 - Regression methods discussed
| > *Part 1 is a recommended pre-requisite
| > •       Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF
| > •       Ensemble Post-Processing: ISLE; RuleLearner
| >
| > Part 4: April 12 - Hands-on demonstration of concepts discussed in Part 3
| > •       Step-by-step demonstration
| > •       Datasets and software available for download
| > •       Instructions for reproducing demo at your leisure
| > •       For the dedicated student: apply these methods to your own data (optional)
| >
| > _______________________________________________
| > R-SIG-Finance at r-project.org mailing list
| > https://stat.ethz.ch/mailman/listinfo/r-sig-finance
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| 
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-- 
Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com