This one-day course will be offered via Zoom only. Attendance is required as it will not be recorded.

Course Summary:
Statistical machine learning is an interdisciplinary research area which is closely related to statistics, computer sciences, engineering, and bioinformatics. Many statistical machine learning techniques and algorithms have proven to be very useful for various scientific areas. This course will cover a number of unsupervised learning techniques for finding patterns and associations in Big Data. These include dimension reduction techniques such as principal components analysis and non-negative matrix factorization, clustering analysis and significance analysis, and network analysis with graphical models. The main emphasis will be on the analysis of real data sets from various scientific fields. The techniques discussed will be demonstrated in R.

This course is intended for researchers who have some knowledge of statistics and machine learning, and want to be introduced to relatively more advanced statistical machine learning topics.

Prerequisite:
Participants should be familiar with matrix linear algebra, linear regression and basic statistical and probability concepts, as well as some familiarity with R programming.

Register

The NC TraCS Institute's new cycle of Clinical and Translational Science (CTS) Pilot funding has been announced. Proposals are due July 8, 2025.

First released in 2022, CTS Pilots are focused on translational science. In preparation for this cycle of CTS Pilots, NC TraCS is holding 1-hour information sessions for investigators interested in learning more about this funding opportunity. Join the CTS Pilot Program leadership to hear about this pilot funding opportunity and have your questions answered.

Presenters
David Peden, MD, MS
CTS Pilot Program Module Lead, NC TraCS Institute
Senior Associate Dean for Translational Research, UNC School of Medicine

David Carroll, PhD
Director of Research Funding Development, NC TraCS Institute

register