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On-Demand Webinar

Principal Component Analysis for Machine Learning

Join SAS Research Statistician Developer Funda Gunes, PhD, as she delves into the basics of principal component analysis for machine learning. She’ll cover what principal component analysis is, her view of the math behind it, as well as how it’s used in the real world and why. Discussion will include:

  • Reducing dimensionality – reducing memory, filtering noise, decreasing redundancy and speeding processing time of other learning algorithms.
  • Visualizing higher dimensional data and detecting outliers.
  • Solving issues of multicollinearity in high dimensional data by using principal components instead of original inputs.

This webinar is part of our Machine Learning webinar series and one of three that will discuss specific types of analytics that can be used in machine learning.

Meet the Speaker

Funda Gunes

Funda Gunes, PhD, researches and implements new data mining and machine learning approaches for SAS. In her previous role at SAS, she helped promote SAS statistical procedures and establish customer relationships with a direct link to R&D by writing papers, creating web examples, and giving expository talks on new modeling and estimation techniques in SAS. Her research interests include penalized regression models, linear/nonlinear mixed models, Bayesian analysis, neural networks, ensemble models and parallelization of machine learning algorithms on distributed data. Gunes has a PhD in statistics from North Carolina State University.

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