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 Biomechanical Path to Osteoarthritis Following Knee Injury and a GAIT-way to Improved Outcomes

Join the UNC School of Medicine Thurston Arthritis Research Center for a UNC Core Center for Clinical Research (CCCR) Speaker Series seminar featuring Brian Pietrosimone, PhD, ATC. Pietrosimone is an Associate Professor in the Department of Exercise and Sport Science at UNC-Chapel Hill and the Director of MOTION Science Institute.

Optimal movement is paramount to maintaining joint health. The development and progression of osteoarthritis has been linked, in part, to altered mechanical joint loading. Traumatic knee injuries are known to lead to changes in walking gait that may alter knee tissue loading and accelerate the development of osteoarthritis. This presentation will specifically describe: i) the aberrant gait biomechanics that we have measured following anterior cruciate ligament injury; ii) the links between these aberrant gait biomechanics and deleterious knee tissue changes related to osteoarthritis development; and iii) some emerging ideas for mitigating aberrant gait biomechanics following anterior cruciate ligament injury.

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