Machine Learning Boot Camp (Live-stream, virtual)

Analyzing Biomedical and Health Data
10:00 am
4:00 pm
Monday
8
June
2020
Add to Calendar:
Live-stream, remote training.
Noah Simon, PhD, University of Washington; Yifei Sun, PhD, Columbia; Cody Chiuzan, PhD, Columbia
Training
Department of Environmental Health Sciences
Columbia SHARP Training Program
Open to the Public
**Due to the uncertainty in the coming months around COVID-19, we are transitioning the Machine Learning Boot Camp to a live-stream, virtual training for this summer. With so many conferences and events being cancelled, we hope that offering this training to be “in-person” via a live-stream, remote format will allow our scientific community to continue learning and developing professionally together. We are working hard on integrating these hands-on skills to an interactive online format.**

The Machine Learning Boot Camp is a two-day intensive boot camp of seminars combined with hands-on R sessions to provide an overview of concepts, techniques, and data analysis methods with applications in biomedical research.

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.

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.

PREREQUISITES AND REQUIREMENTS
There are three prerequisites/requirements to attend:

1. Each participant must have an introductory background in statistics.
2. Each participant must be familiar with R. The main software used for the workshop will be R/RStudio, therefore participants must have a basic understanding of this software prior to attending the Training.
3. Each participant is required to have 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.

INSTRUCTORS
- Noah Simon, PhD, Department of Biostatistics, School of Public Health, University of Washington.
- Yifei Sun, PhD, Department of Biostatistics, Mailman School of Public Health, Columbia University.
- Cody Chiuzan, PhD, Department of Biostatistics, Mailman School of Public Health, Columbia University.

ADDITIONAL INFORMATION
- Subscribe for updates: http://eepurl.com/dLa_iU
- Email our team: Columbia.MachineLearning@gmail.com

Capacity is limited. Registration is required to attend.

Contact:

JoAnn Schneider, MPA