Machine Learning Boot Camp

Analyzing Biomedical and Health Data
10:00 am
5:00 pm
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Virtual, live-stream
Department of Environmental Health Sciences
SHARP Training Program
Open to the Public
The Machine Learning Boot Camp is a two-day intensive boot camp of seminars combined with hands-on R labs and data applications to provide an overview of statistical concepts, techniques, and data analysis methods with applications in biomedical research.

This training will provide a broad introduction to machine learning methodology with applications in biomedical research. Taught by a team of biostatisticians, the Boot Camp will integrate seminar lectures with hands-on R lab sessions to put concepts into practice. Emphasis will be given to supervised (e.g., penalized methods, classification and decision trees, survival forests) and unsupervised methods (e.g., clustering algorithms, dimensionality reduction) with numerous case studies and biomedical applications. The workshop will conclude with an overview and demonstration of 'deep learning' algorithms.

By the end of the boot camp, participants will be familiar with the following topics:
-Penalized Regression Methods (Ridge and Lasso)
-Support Vector Machines
-Decision Trees (Random Forest)
-Predicting Survival Outcomes (Cox Regression/Lasso, Survival Forests)
-Clustering Algorithms
-Principal Component Analysis (PCA)
-Deep Learning - An Illustrative Overview

Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.

There are three prerequisites/requirements to attend:

1. Each participant must have an introductory background in statistics.
2. Each participant is encouraged to be familiar with R. The main software used for the workshop will be R/RStudio, therefore we strongly recommend that participants have a basic understanding of this software prior to attending the Training.
3. Each participant is required to bring a personal laptop with R/RStudio installed prior to the first day of the workshop, as all lab sessions will be done on your personal laptop. R is available for free download and installation on Mac, PC, and Linux devices.

For an R installation guide, please visit our website.

- Noah Simon, PhD, Department of Biostatistics, School of Public Health, University of Washington.
- Jean Feng, PhD, Department of Biostatistics and Epidemiology, School of Medicine, University of California, San Francisco.
- Cody Chiuzan, PhD, Department of Biostatistics, Mailman School of Public Health, Columbia University.

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Capacity is limited. Registration is required to attend.


Machine Learning Boot Camp