Machine Learning Boot Camp: Analyzing Biomedical and Health Data



Machine Learning Boot camp - Columbia University SHARP TrainingCourse at capacity! Join us for the next virtual, live-stream Machine Learning Boot Camp: June 8-9, 2020


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.      


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Summer 2020 dates: Live-stream, online training June 8-9, 2020; 10:00am - 4:00pm EDT

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.


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 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. Please review the R Installation Guide below.


Basic R knowledge is required for the boot camp as noted in prerequisites above. 

  • R Installation Guide: R is the free software programming language we will use in the boot camp. Use this installation guide to choose the correct version for your laptop (Mac/Windows) and install it prior to the first day of the boot camp.

  • Introduction to R: A free edX class on R fundamentals using Datacamp platform 

If you have any specific questions about R and R studio in the context of the Machine Learning Boot Camp, please email us.


Noah Simon, PhD, Department of Biostatistics, School of Public Health, University of Washington. Dr. Simon’s methodological interests include computationally efficient methods for predictive modeling with high-dimensional, complex data, and the design of adaptive clinical trials.

Yifei Sun, PhD, Department of Biostatistics, Mailman School of Public Health, Columbia University. Dr. Sun’s research interests include survival and longitudinal data analysis and statistical machine learning for time-to-event data.

Cody Chiuzan, PhD, Department of Biostatistics, Mailman School of Public Health, Columbia University. Dr. Chiuzan’s research interests concern development of adaptive early-phase designs for oncology trials, including questions on the optimal study designs and endpoints for early-phase immune- and targeted-oncology agents. Dr. Chiuzan is the Director for Educational Initiatives of CTSA Biostatistics, Epidemiology and Research Design Resource (BERD) Resource.


Training scholarships are available for the Machine Learning Boot Camp.


COVID-19 Update: The Machine Learning Boot Camp will no longer take place in person due to the COVID-19 pandemic. The Boot Camp will instead be a live-stream, remote training that takes place over live, online video on June 8-9, 2020 from 10am EDT - 4pm EDT. Please note this training is not a self-paced, pre-recorded online training. 


"This boot camp was excellent in providing an introduction to machine learning. The quality of instruction was outstanding." - Victoria C., Research Biostatistician at Weill Cornell Medicine, 2019

"This is a great graduate-level workshop to understand the similarities and differences between traditional statistical modeling and machine learning. The level and pace are good, as are the Rmarkdown examples." - Anonymous Faculty member, 2019

"I enjoyed the ML boot camp. The instructors are highly knowledgable of statistics and ML as well as helpful. The intro to R session (1 hour) was not long enough and it was very rushed due to the fact that we were scheduled to go right into the actual boot camp workshop immediately afterwards. The days were long but not overly taxing. Overall, for someone with no background in using R or ML, I feel that I learned a tremendous amount! Thanks!" - Greg D, Faculty member at University of Delaware, 2019

"Instructors are super dedicated and training materials are well prepared. I definitely feel more confident in applying statistical learning methods in my work." - Xian W, Research Biostatistician at Weill Cornell Medicine, 2019 

"An excellent bootcamp that gives a good overview of machine learning as a concept as well as specific approaches." - Haotian W., Postdoc at Columbia Mailman School of Public Health, 2019

"This was a great boot camp for people with a firm understanding of principles of statistics and machine learning, who are looking to deepen their knowledge, understanding, and application of machine learning in their research projects." - Marta J., Assistant Research Scientist at UCSD, 2019

"It was a great introduction to ML and it provided me with the right tools to apply these techniques in my own research." - Sujith R., Faculty member at University of Mississippi, 2019


COVID-19 Update: With the training being offered virtually, we are passing along any and all costs saved to attendees.

  Early-Bird Rate (through 4/15/20 4/22/20) Regular Rate (4/16/20 4/23/20 - 5/21/20) Columbia Discount*
Student/Postdoc/Trainee     $1,150  $825 $1,350 $975  10%
Faculty/Academic Staff/Non-Profit Organizations $1,350 $975  $1,550 $1,125 10%
Corporate/For-Profit Organizations $1,550 $1,125 $1,750 $1,275 NA

*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. To access Columbia discount, email for instructions and specify if you are paying by credit card, or internal transfer within Columbia.

Invoice Payment and Group Registrations: If you would prefer to pay by invoice/check, or would like to pay for a group of registrants, please email with details.

Registration Fee: includes course material. Course material will be available to all students after the workshop.

Cancellations: For summer 2020, no administrative fees will be assessed due to the evolving COVID-19 situation. Cancellation notices must be received via email at least 14 days prior to the workshop start date in order to receive a full refund. Please email your cancellation notice to . Due to workshop capacity and preparation, we regret that we are unable to refund registration fees for cancellations after these dates, unless a new COVID-19 restriction is implemented that impedes virtual attendance, in which case any registration cancellation <14 days prior to a training related to COVID-19 restriction beyond your control (institutional policy, shift in work responsibilities, etc.) will be fully refunded and no administrative fee will be assessed. Because of the significant resources required to develop these trainings, you will be asked to submit supporting documentation (e.g. employer email notice, local regulations, etc.) for any COVID-19 related cancellation <14 days before a given training.

If you are unable to attend the training, we encourage you to send a substitute within the same registration category. Please inform us of the substitute via email at least one week prior to the training to include them on attendee communications, updated registration forms, and materials. Should the substitute fall within a different registration category your credit card will be credited/charged respectively. Please email substitute inquiries to . In the event Columbia must cancel the event, your registration fee will be fully refunded. 



Want updates on new Boot Camp details or registration deadlines? Subscribe here.

Questions? Email the Boot Camp team here.

The Machine Learning Boot Camp is hosted by Columbia University's Department of Environmental Health Sciences and Department of Biostatistics in the Mailman School of Public Health, and the Irving Institute for Clinical and Translational Research: Biostatistics, Epidemiology, and Research Design (BERD) Educational Resource.