This course will be offered via Zoom and divided into two parts: September 20 & 22 and October 4 & 6. Attendance is required as the course will not be recorded.

Course Summary:
Statistical machine learning and data mining is an interdisciplinary research area which is closely related to statistics, computer sciences, engineering, and bioinformatics. Many statistical machine learning and data mining techniques and algorithms are very useful for various scientific areas. This short course will provide an overview of statistical machine learning and data mining techniques with applications to the analysis of real data. Supervised learning techniques will be covered, including penalized regression such as LASSO and its variants, support vector machines. 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 want to be introduced to statistical machine learning and data mining, or practitioners who would like to apply statistical machine learning techniques to their problems.

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

Instructor: Yufeng Liu, PhD
Yufeng Liu, PhD, is currently a professor in the Department of Statistics and Operations Research, Department of Biostatistics, and Department of Genetics at UNC-Chapel Hill. His current research interests include statistical machine learning, high dimensional data analysis, personalized medicine, and bioinformatics. He has taught statistical machine learning courses multiple times at UNC, as well as short courses on this subject at Joint Statistical Meetings, ENAR, FDA, and Biostatistics Summer Institutes at the University of Washington.

Registration Fees
- UNC-Chapel Hill Students: $0, with a $20 deposit to hold your spot (deposit is refundable upon your attendance for at least 66% of the course)
- UNC-Chapel Hill Faculty/Staff/Postdoc/Resident/Visiting Scholars: $40

Additional Course Registration
- Registration will close at 12:01 am on 9/17/2022. Once registration closes, no late registrations will be accepted, no exceptions.
- Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
- Zoom link for this course will be sent prior to the course. Registration must be made at least 3 days prior to the course date to receive the Zoom link.
- For questions regarding the status of this class, please contact Jill Stevens at This email address is being protected from spambots. You need JavaScript enabled to view it..

Register

This course will be offered via Zoom and divided into two parts: September 20 & 22 and October 4 & 6. Attendance is required as the course will not be recorded.

Course Summary:
Statistical machine learning and data mining is an interdisciplinary research area which is closely related to statistics, computer sciences, engineering, and bioinformatics. Many statistical machine learning and data mining techniques and algorithms are very useful for various scientific areas. This short course will provide an overview of statistical machine learning and data mining techniques with applications to the analysis of real data. Supervised learning techniques will be covered, including penalized regression such as LASSO and its variants, support vector machines. 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 want to be introduced to statistical machine learning and data mining, or practitioners who would like to apply statistical machine learning techniques to their problems.

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

Instructor: Yufeng Liu, PhD
Yufeng Liu, PhD, is currently a professor in the Department of Statistics and Operations Research, Department of Biostatistics, and Department of Genetics at UNC-Chapel Hill. His current research interests include statistical machine learning, high dimensional data analysis, personalized medicine, and bioinformatics. He has taught statistical machine learning courses multiple times at UNC, as well as short courses on this subject at Joint Statistical Meetings, ENAR, FDA, and Biostatistics Summer Institutes at the University of Washington.

Registration Fees
- UNC-Chapel Hill Students: $0, with a $20 deposit to hold your spot (deposit is refundable upon your attendance for at least 66% of the course)
- UNC-Chapel Hill Faculty/Staff/Postdoc/Resident/Visiting Scholars: $40

Additional Course Registration
- Registration will close at 12:01 am on 9/17/2022. Once registration closes, no late registrations will be accepted, no exceptions.
- Cancellation/ Refund Policy: A full refund will be given to those who cancel their registration no later than 10 days prior to the course. If you cancel within 10 days prior to the class, no refund will be given. Please allow 30 days to receive your refund.
- Zoom link for this course will be sent prior to the course. Registration must be made at least 3 days prior to the course date to receive the Zoom link.
- For questions regarding the status of this class, please contact Jill Stevens at This email address is being protected from spambots. You need JavaScript enabled to view it..

Register

This online training session will provide an introduction to qualitative research methods. Participants will have the opportunity to ask questions and share experiences conducting qualitative research.

Topics:

• Differences between quantitative and qualitative research
• Qualitative research methodologies
• Strategies for qualitative data collection
• Methods of qualitative data analysis

Presenters:
MaryBeth Grewe, MPH
Engagement and Qualitative Research Specialist
NC TraCS Institute

Simone Frank, MPH
Community Engagement and Outreach Specialist
NC TraCS Institute

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Non-linear link functions cause some difficulty in interpreting parameters in generalized mixed models. This presentation will cover the interpretation of both regression coefficients and variance components in generalized mixed models.

Regression coefficients for within- and between-cluster covariates have different interpretations that will be explained. Interpretation of variance components, within and between, will be covered.

The focus will be on logistic and log-linear models, although the ideas apply to other link functions. Numerical examples will be presented.

Presenter: Bahjat Qaqish, PhD
Professor, Department of Biostatistics
UNC Gillings School of Global Public Health

Format: ~1 hour talk, 30 min Q&A

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The NC TraCS Biostatistics Seminar Series provides more in-depth discussion of select biostatistical topics for clinical and translational researchers who have basic quantitative training in biostatistical methods. Join us this fall for seminars on power analysis & sample size planning, parameter interpretation in generalized mixed regression models, basic steps in questionnaire development, and choosing the right graphs and tables for your data.

Impact Of Handovers Of Anesthesia Care On Morbidity And Mortality

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Melanie Meersch-Dini, MD
Professor
Department of Anesthesiology, Intensive Care and Pain Medicine
University Hospital Münster, Germany

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Fall 2022 Seminar Series Co-Sponsored by the UNC Gillings Department of Nutrition and the UNC Nutrition Obesity Research Center

Virtual Seminar, A Career in Biopharmaceutical Industry, with Juhaeri Juhaeri, PhD, Vice President and Head of Epidemiology and Benefit-Risk, Sanofi.

Register