Columbia University Machine Learning Boot Camp: Analyzing Biomedical and Health Data

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.

The Machine Learning Boot Camp instructors are a team of biostatisticians from leading institutions, all with expertise in applying machine learning to biomedical research.

This two-day intensive training will provide a broad introduction to machine learning methodology, integrating 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.

There is a limited capacity for the Boot Camp. Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.

Prerequisites/Requirements: It is required that each participant have an introductory background in statistics, familiarity with R/R Studio, and 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. If you have never used R before a brief tutorial/refresher will be provided on the first day of Training. R is available for free download and installation on Mac, PC, and Linux devices. Please review the R Installation Guide on our website.

More Information:

• Paid registration required through RegFox: https://columbia-workshops.regfox.com/machine-learning-boot-camp
• More information about training, registration deadlines, fees, and logistics can be found on our website at https://www.mailman.columbia.edu/machine-learning
• Email us at Columbia.MachineLearning@gmail.com