Statistical Machine Learning for Biomedical Data
A 2-day intensive workshop by Dr. Noah Simon, University of Washington
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Date: May 25-26, 2020
Location: uOttawa TBD
Cost: TBA – reduced cost for registered students
Hosted by the Ottawa Hospital Research Institute, Big Life Lab and Bruyère Centre for Individualized Health and with support from Canadian Society for Epidemiology and Biostatistics at University of Ottawa
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About the workshop:
Do you have a background in biostatistics and interested in machine learning but don’t know where to start? This 2-day hands-on workshop is for you!
Dr. Noah Simon from the University of Washington will present his highly regarded 2-day intensive workshop. You’ll learn about how traditional statistical methods like logistic regression can be used for high dimensional data (data with many variables) and then move onto machine learning methods such as random forests, support vector machines, (gradient) boosting using trees and neural networks.
New machine learning methods will be related to more classical statistical approach – all designed for an audience with familiarity with statistical approaches and demonstrated using biomedical big data. The focus on how these machine learning ideas/methods can be used for predictive analytics using observational data. Throughout the course, Dr. Simon will focus on common pitfalls in the supervised analysis of Biomedical Big Data and how to avoid them. The course will include interactive discussions, "Challenge Questions", R package recommendations, and other tools to help participants actively engage with applying these methods in biomedical scenarios.
By the end of the workshop, participants will be able to….
1) Understand the bias/variance trade-off and its various applications.
2) Understand the use of split-sample validation for tuning bias/variance and evaluating performance.
3) Have some intuition for the various regression/classification methods.
4) Understand how model aggregation techniques can be applied.
5) Understand how supervised techniques can be applied to the construction of predictive and prognostic biomarkers.
6) Have some working knowledge for how to apply these tools in common biomedical scenarios.
7) Understand the main ideas in deep learning, how they relate to classical statistical ideas, and some scenarios where they may be useful.
More about Dr. Simon:
Noah is a Stanford PhD, trained with Dr. Rob Tibshirani. He lives and works in at the intersection of machine learning and traditional statistics and machine learning as well as being an extraordinary teacher.
For additional information email: firstname.lastname@example.org