Machine Learning: Analyzing Biomedical and Health Data
Explore machine learning in this two-day boot camp through seminars and hands-on R labs, mastering statistical concepts, techniques, and data analysis methods for biomedical research.
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Course Description
This two-day intensive boot camp offers a comprehensive introduction to machine learning methods, combining seminars and hands-on R labs with a focus on biomedical research applications.
- Apply supervised machine learning methods, including penalized regression (Ridge and Lasso), classification models (e.g., Support Vector Machines), and tree-based methods (e.g., decision and regression trees) in biomedical research.
- Utilize unsupervised learning techniques such as clustering algorithms and Principal Component Analysis (PCA) to identify patterns and reduce dimensionality in complex biomedical datasets.
- Implement hands-on data analysis using R, integrating seminar concepts with practical lab sessions to strengthen understanding of machine learning applications.
- Explore deep learning fundamentals, including dense and convolutional neural networks, through demonstrations and real-world biomedical examples.
To contact support for this course, please email [email protected].
Course Prerequisites
The Machine Learning Boot Camp welcomes investigators from all institutions and career stages, with a focus on trainees and early-stage researchers. Participants must have a basic background in statistics, be familiar with R and RStudio, and have a personal laptop with a free RStudio Cloud account for hands-on lab sessions.
What You Will Learn
By the end of this training, learners will be able to:
- Apply supervised machine learning techniques, including penalized regression methods (Ridge and Lasso), classification models (e.g., Support Vector Machines), and tree-based methods (e.g., decision and regression trees) in biomedical research.
- Utilize unsupervised learning approaches such as clustering algorithms and Principal Component Analysis (PCA) to uncover patterns and reduce dimensionality in complex datasets.
- Implement hands-on data analysis using R and RStudio Cloud, integrating statistical concepts through practical lab sessions and real-world biomedical case studies.
- Explore foundational deep learning concepts, including dense and convolutional neural networks, and understand their applications in biomedical research.
Instructors
Arjun Sondhi, PhD is an 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 healthcare data.