2020 SORA-TABA Annual Workshop - Statistical Machine Learning for Biomedical Data
Abstract: Dr. Noah Simon from the University of Washington will present a 2-day intensive workshop. He will present a number of supervised learning methods that can be applied to Biomedical Big Data: In particular he will cover penalized approaches to regression and classification; as well as support vector machines, tree-based methods, and deep learning.
Dr. Simon will consider the analysis of “high-dimensional Omics” data sets. These data are typically characterized by a huge number of molecular measurements (such as genes) and a relatively small number of samples (such as patients). In addition, he will discuss the use of these tools in the development of prognostic and predictive biomarkers.
Throughout the course, he will focus on common pitfalls in the supervised analysis of biomedical big data and how to avoid them. The course will include interactive discussions/”Challenge Questions” to help participants actively engage with applying these tools in biomedical scenarios.
To register and learn more about the workshop, please visit our registration page. Registration ranges from \$20.00-\$55.00!