Machine Learning Boot Camp

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, 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) 
• Classification Models (e.g., Support Vector Machines) 
• Tree Based Methods (Decision/Regression Trees) 
• Clustering Algorithms 
• Principal Component Analysis (PCA) 
• Deep Learning – Introduction to dense and convolutional neural networks 

Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements to attend this training:

1. Each participant must have an introductory background in statistics (i.e., linear and logistic regression).
2. Each participant must be familiar with R. The main platform used for the workshop will be RStudio Cloud, therefore we strongly recommend that participants have a basic understanding of R/RStudio prior to attending the Training. 
3. Each participant is required to have a personal laptop and a free, basic RStudio Cloud account prior to the first day of the workshop. All lab sessions will be done on this platform.

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

Lead Instructor: Arjun Sondhi, PhD assistant professor in the quantitative intelligence department at the Feinstein Institutes for Medical Research. Trained as a biostatistician, his expertise is in machine learning and causal inference methods applied to health care data.

Stephen Scott Jones, MS, Associate Biostatisician at The Feinstein Institute for Medical Research Northwell Health

Training scholarships are available for the Machine Learning Boot Camp.

Summer 2026: The Machine Learning Boot Camp will be a hybrid setup with an in-person training at the Columbia University Irving Medical Campus in NYC and simultaneously livestreamed for remote attendees taking place on June 1-2, 2026 from 10:00am - ~5:00pm EDT. Please note this training is not a self-paced, pre-recorded online training. All training start and end times are in EDT.

More information on travel, lodging, and getting around NYC.

"The Machine Learning Boot Camp provides thorough explanations of key concepts crucial to understanding how to implement these method on your research team. You also work on labs where you can apply the information! Very helpful." - Research Analyst at Center for Innovation through Data Intelligence, 2025

"The Machine Learning Boot Camp provided a solid foundation in key concepts and practical applications. The instructing team was knowledgeable, engaging, and supportive throughout the training." - Postdoc at CUNY, 2025

"The Machine Learning Boot Camp was extremely well organized and effectively taught with super clear slides, excellent R syntax, helpful hands-on training, great in-the-moment feedback from the instructors, and even a fascinating guest lecture with an impactful real-world example presented on the final day." - Faculty Member at Columbia University Irving Medical Center, 2025

"As a neurologist focused on research, the boot camp was a superb introduction to machine learning, and it gave me the tools I need to start creating my own models in my research and to improve my critical thinking." - PhD candidate at HM CINAC Integral A.C. Neuroscience Center, 2023

"As an online attendee, the machine learning boot camp was well organized. The trainers accommodated all the attendees at their various levels of understanding of machine learning. It was well worth the time spent training." - Senior lecturer at University of Nairobi, 2023

"Such a great experience with enthusiastic instructors. I had dabbled in machine learning in the past but this course helped to contextualize the methods and got sufficiently into the mathematics. I feel confident in being able to generate questions and answer them with appropriate machine learning methods." - Postdoctoral fellow at Stanford/VA, 2023

Registration Fee is based on your category. This is a hybrid training, meaning attendees can choose to attend the live training either 1) in-person in NYC or 2) virtually livestream via Zoom. The in-person registration fee includes course material, breakfast, and lunch on training days. Lodging and transportation are not included. The virtual registration fee includes course material, which will be made available to all participants both during and after the conclusion of the training. 

2026 Registration Category Rates: 

• Student/Postdoc/Trainee: 
  • Early-bird rate: $1,195 (in-person); $995 (virtual)
  • Regular rate: $1,395 (in-person); $1,195 (virtual)
• Faculty/Academic Staff/Non-Profit Organizations/Government Agencies: 
  • Early-bird rate: $1,395 (in-person); $1,195 (virtual)
  • Regular rate: $1,595 (in-person); $1,395 (virtual)
• Corporate/For-Profit Organizations: 
  • Early-bird rate: $1,595 (in-person); $1,395 (virtual)
  • Regular rate: $1,795 (in-person); $1,595 (virtual)

$200 early-bird discount is automatically applied if you register before the April 15  deadline.  

• $200 Early-bird Discount: This is automatically applied if you register before the April 15 early-bird deadline.  

• 10% Columbia Discount: This is valid for any active student, postdoc, staff, or faculty at Columbia University. If paying by credit card, use your Columbia email address during the registration process to automatically have the discount applied. If paying by internal transfer within Columbia, see below.  

• 10% Mailman Alumni Discount: This is valid for any individual who graduated from the Columbia University Mailman School of Public Health. To access the Mailman Alumni discount and receive a registration code, please email sharp_program@cumc.columbia.edu your graduation year and degree.  

• Group discounts are available for organizations sending 5+ participants. Please contact us directly at sharp_program@cumc.columbia.edu for more information

If paying by internal transfer within Columbia, submit this Columbia Internal Transfer Request form (link to form coming soon) to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

If you would prefer to pay by invoice/check, please submit this Invoice Request form (link to form coming soon) to receive further instructions. Please note: filling out this form is not the same as registering for a training and does not guarantee a training seat.

Cancellation notices must be received via email at least 30 days prior to the training start date in order to receive a full refund, minus a $75 administrative fee. Cancellation notices received via email 14-29 days prior to the training will receive a 75% refund, minus a $75 administrative fee. Please email your cancellation notice to Columbia.MachineLearning@gmail.com. Due to workshop capacity and preparation, we regret that we are unable to refund registration fees for cancellations less than 14 days prior to the 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 so we can include them on attendee communications, gather registration details, and provide materials. Should the substitute fall within a different registration category (e.g., you are a faculty member and they are a postdoc), the credit card on file will be credited/charged respectively. Please email substitute inquiries to Columbia.MachineLearning@gmail.com. In the event Columbia must cancel the event, your registration fee will be fully refunded.