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.

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The Office of the Vice Chancellor for Research (OVCR) invites all members of the research community to a once-per-semester live update session.

What to Expect for the April Session:
IRB Process Updates: Senior Associate Vice Chancellor Andy Johns and Director of the Office of Human Research Ethics Carley Emerson will provide the latest information and changes to IRB procedures.
Research Enterprise Updates: Vice Chancellor Penny Gordon-Larsen will share general news and developments impacting Carolina’s research activities.
Q&A: There will be a dedicated Q&A session for participants to ask questions to any member of the OVCR leadership team.

Please submit your questions in advance to facilitate a smooth discussion. You can submit them during registration or by email to This email address is being protected from spambots. You need JavaScript enabled to view it..

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