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"Unsupervised Ensemble Learning" by Dustin Johnson #176

scientificbruno opened this Issue Jul 27, 2017 · 0 comments


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scientificbruno commented Jul 27, 2017


Unsupervised learning involves determining a hidden structure (typically groupings or clusters) from unlabelled data. Without labels, we don't have a measure of accuracy to determine which model to trust more. In this workshop, we will demonstrate how more minds work better than one and let a consensus of experts decide the ideal clustering.

Brought to you by Applied Quantitative Methods.

Time and Place

Where: Room 7010, Library Research Commons, SFU Burnaby Campus

When: Tuesday, September 12th, 2017 at 3:00 PM



Required Preparation

I will have assumed individuals have had some experience with the implementation of an unsupervised algorithm, such as K-means, and understand the difference between the supervised and unsupervised paradigms of learning. In addition, some familiarity with statistics (averages and variances) would be helpful. We will not be covering the particular unsupervised algorithms themselves, but resources will be provided for those who are interested. The purpose of this workshop is to understand how to combine unsupervised learners together to achieve stability and robustness of performance.

Familiarity with scientific computing in Python 3 with the numpy, scikit-learn, and pandas modules would be quite helpful.

If you do not have the software above installed, please refer to the Installation section in


Lessons Notes: TBA

Etherpad: TBA

@scientificbruno scientificbruno added this to the Fall 2017 milestone Jul 27, 2017

@scientificbruno scientificbruno changed the title from "TBA" by Applied Quantitative Methods to "To Be Determined" by Applied Quantitative Methods Jul 27, 2017

@scientificbruno scientificbruno changed the title from "To Be Determined" by Applied Quantitative Methods to "Unsupervised Ensemble Learning" by Dustin Johnson Aug 1, 2017

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