Machine Learning November 2018

Event Phone: 610-715-0115

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Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 2-Day Seminar 
Taught by Kevin Grimm, Ph.D.

Machine learning has emerged as a major field of statistics and data analysis where the goal is to create reliable and flexible predictive models. These methods have gained much attention for analyzing large datasets that may be composed of several hundred variables and many thousands (perhaps millions) of participants. In these situations, machine learning algorithms attempt to identify key variables needed in the predictive model, and several techniques search for nonlinear associations and interactive effects.

While machine learning techniques have been most attractive for large datasets, these same techniques can be useful in smaller datasets for the same reasons–to create simpler and more reliable predictive models, and to search for nonlinear and interactive effects. These techniques are also a natural follow-up to standard hypothesis-driven statistical analyses (e.g., multiple regression) to search for additional important patterns in the data.

The first day starts with an overview of machine learning and continues with an introduction to the basic techniques. Topics for day 1 include cross-validation, multiple regression, and basic variable selection methods, as well as an overview of the R statistical framework. The second day focuses on advanced variable selection methods for regression analysis. Topics include multivariate adaptive regression splines, lasso regression, classification and regression trees, bagging, and random forests. Throughout the course, participants gain experience with these methods through hands-on exercises.

Venue:  

Venue Phone: 312-836-5000

Venue Website:

Address:
350 West Mart Center Drive, Chicago, Illinois, 60654, United States