Statistical Machine Learning for Biomedical Data with Dr. Noah Simon
UPDATE: Due to the novel virus COVID-19, we are in the process of changing the format to be an interactive, virtual workshop.
NEW REGISTRANTS: Please do not register at this time. We will be working over the next couple of weeks to adjust costs related to the format change. Please check back later.
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 is 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.
Hosted by the Big Life Lab at the Ottawa Hospital Research Institute, the Bruyère Centre for Individualized Health and the Faculty of Medicine, University of Ottawa with support from Canadian Society for Epidemiology and Biostatistics at uOttawa and R Ladies Ottawa.
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. Learn more about Dr. Simon at
Please note there is limited space available.
Meals and coffee breaks included with registration. Lunch and coffee breaks (morning and afternoon) for both days.
All attendees will be required to bring proof of employment or association status in accordance with the ticket type they purchased.
There will be NO REFUNDS once you purchase a ticket.