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.