Course is at capacity! Join the waitlist for the next live-stream Machine Learning Boot Camp on June 16-17, 2022.
The Machine Learning Boot Camp is a two-day intensive boot camp of seminars combined with hands-on R labs and data applications to provide an overview of statistical concepts, techniques, and data analysis methods with applications in biomedical research.
Summer 2022 dates: Live-stream, online training June 16-17, 2022; 10am - 5pm 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, 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)
Principal Component Analysis (PCA)
Deep Learning – Introduction to dense and convolutional neural networks
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:
Each participant must have an introductory background in statistics (i.e., linear and logistic regression).
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.
Each participant is required to have a free, basic RStudio Cloud account prior to the first day of the workshop, as 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 us.
Noah Simon, PhD, Associate Professor, 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.
Jean Feng, PhD, Assistant Professor, Department of Epidemiology and Biostatistics, University of California, San Francisco. Dr. Feng's research interests include the interpretability and reliability of machine learning methods for biomedical applications, particularly those involving black-box models.
Cody Chiuzan, PhD, Associate Professor, Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health.Dr. Chiuzan’s research interests concern development of adaptive early-phase designs for oncology trials, and using real-world evidence to improve clinical outcomes and guide the transfer of knowledge among non-experimental studies.
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 16-17, 2022 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training.
"This was a fantastic training with a very engaged group of students and teachers. There was a perfect balance between theoretical underpinnings and practical advice."-Faculty Member from Harvard University, August 2021 Virtual Training
"The ML boot camp was a wonderful experience that provide a thorough review from the basics to advanced techniques for sophisticated analysis of data. I found it extremely accessible as a non-expert in the field. "-Postdoc at Memorial Sloan Kettering Cancer Center, August 2021 Virtual Training
"This was really a phenomenal course, I don't think I could have had a better experience in terms of learning a wide range of machine learning techniques."-Postdoc at The University of Pittsburg Medical Center August 2021 Virtual Training
"The ML Boot Camp is a wonderful introduction to machine learning in a way that is accessible to non-computer scientists." -Postdoc at the Wistar Institute, August 2021 Virtual Training.
"This bootcamp is an excellent introduction to machine learning. It covers many important fundamental concepts of machine learning and its nuances are well-taught during the lab sessions. The instructors are also well-versed in the field and do a great job explaining these complex concepts." - Faculty member from Harvard University, August 2020 virtual training
"The workshop introduces up-to-date concepts and provide training using timely examples with well-integrated insights." - Postdoc from MIT, June 2020 virtual training
COVID-19 Update: With the training being offered virtually, we are passing along any and all costs saved to attendees.
|Early-Bird Rate (through 4/15/22)||Regular Rate (4/16/22 - 6/1/22)||Columbia Discount*|
|Faculty/Academic Staff/Non-Profit Organizations/Government Agencies||$1,025||$1,225||10%|
*Columbia Discount: This discount is valid for any active student, postdoc, staff, or faculty at Columbia University. To access Columbia discount, email Columbia.MachineLearning@gmail.com for instructions and specify the following: 1) your registration category from the table above, 2) if you are paying by credit card, or internal transfer within Columbia, and 3) if an internal transfer, indicate the registration category from the table above, and if grant funds are being used.
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 Columbia.MachineLearning@gmail.com with the following details: 1) full attendee name(s) and applicable registration category from the table above, and 2) payment method (credit card, invoice, wire).
Registration Fee: This fee includes course material, which will be made available to all participants both during and after the conclusion of the training.
Cancellations: 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 <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 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 Columbia.MachineLearning@gmail.com. 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.
"Great instructors, interactive, and an excellent short course!" - Corporate Staff member at Johnson & Johnson, August 2020 virtual training.
"The bootcamp was an informative graduate-level introduction to ML methods and covered a breadth of topics in a short amount of time. The instructors gave very clear presentations and coding demos, and I appreciated how approachable they were in terms of answering questions and offering advice." - Postdoc at the NIH, August 2020 virtual training
"Exceeded my expectations. Covered both conventional and cutting-edge methods of machine learning with both depth and breadth, implemented on real-world examples." - Student from Mount Sinai, June 2020 virtual training
"The instructors were friendly and approachable, providing just the ML overview I needed: methods grouped by use; how each algorithm works; strengths and limitations. The lab practice--with TA's available to assist--was confidence-building and provided take-aways I can apply to my own data sets." - Student from the University of Tennessee Health Science Center, August 2020 virtual training
"This boot camp was excellent in providing an introduction to machine learning. The quality of instruction was outstanding." - Victoria C., Research Biostatistician from Weill Cornell Medicine, 2019
"I enjoyed the ML boot camp. The instructors are highly knowledgeable 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 from 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 from 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 from Columbia University, 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 from 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 from University of Mississippi, 2019