R help - Mar 2013 - Tomorrow: The Evolution of Regression from Classical Linear Regression to Modern Ensembles (hands-on)

Tomorrow, Friday March 15

Maybe you missed Part 1 of "The Evolution of Regression Modeling from
Classical Linear Regression to Modern Ensembles " webinar series, but you
can still join for Parts 2, 3, & 4
> Register Now for Parts 2, 3, 4:
https://www1.gotomeeting.com/register/500959705
> 
> Course Outline: Overcoming Linear Regression Limitations
> 
> 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.
> 
> Part 2 (Hands-on): March 15, 10-11am PST - Hands-on demonstration of
concepts discussed in Part 1 (Classical Regression, Logistic Regression,
Regularized Regression: GPS Generalized Path Seeker, Nonlinear Regression: MARS
Regression Splines)
> 
> 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 1 recording:
http://www.salford-systems.com/videos/tutorials/805-the-evolution-of-regression-modeling-part-1
> 
> Part 3: March 29, 10-11am PST - 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, 10-11am PST - 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)
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