Summer 2023 dates: Hybrid training (In-person and simultaneous livestream for remote attendees) June 22-23, 2023; 10am - ~5pm EDT.
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.
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
Audience and Requirements
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:
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 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.
Instructors
Cody Chiuzan(link is external and opens in a new window), PhD, Associate Professor, Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health.
Jean Feng(link is external and opens in a new window), PhD, Assistant Professor, Department of Epidemiology and Biostatistics, University of California, San Francisco.
Noah Simon(link is external and opens in a new window), PhD, Associate Professor, Department of Biostatistics, School of Public Health, University of Washington.
Additional Information
View the training website for more details.
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Contact the Machine Learning Boot Camp team.
Capacity is limited. Paid registration is required to attend.
