Wed, Jun 30 | Webinar

Human Bias in Machine Learning: How Well Do You Really Know Your Model? & Principal Component Analysis Demystified

Two great stat presentations! Jim Box of SAS Institute will show how SAS Viya can be used to examine and eliminate bias in machine learning. Caroline Walker of Warren Rogers LLC will present a deep dive into Principal Component Analysis.
Human Bias in Machine Learning: How Well Do You Really Know Your Model?  & Principal Component Analysis Demystified

Time & Location

Jun 30, 12:00 PM – 1:00 PM EDT
Webinar

Abstract

Identifying Sources of Bias in Machine Learning Models

by Jim Box

Artificial Intelligence systems and Machine Learning models are having a dramatic impact on many industries. However, with every story of success, we are seeing instances of biased results doing real harm.  In this session, we will look at some of the sources of bias and unexpected results, and explore ways to mitigate the negative impact of these models.

Principal Component Analysis Demystified

by Caroline Walker

Have you used or thought of using Principal Component Analysis (PCA) as a feature extraction method in your machine learning pipelines, but wished for a better understanding of what a principal component is and how it’s obtained? This talk will take a deep dive into a small dimensional data set, present a visual explanation of the role played by eigenvalues and eigenvectors when PCA is applied, and illustrate how what you start with leads to what you end with, what the advantages are, and what could get lost along the way.

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June 30

Noon ET

Two Stat Talks: Machine Learning & PCA
Presenters: Jim Box and Caroline Walker