This two-day intensive 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, principal components) with numerous case studies and biomedical applications. The workshop will conclude with a brief overview on ‘deep learning’ approaches DOs and DON’Ts.
- Machine Learning Boot Camp: Analyzing Biomedical and Health Data
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
- Principle Component Analysis (PCA)
- Deep Learning – An Overview
Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.
|Date||08 Jun 2020|
|Location||Columbia University |
New York, New York, USA