ResBaz2021

Using Generalized Low Rank models to deal with real-world data

Wednesday, May 19th, 2021 9:00-10:00

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The goal of this workshop is to introduce Generalized Low Rank models (GLRMs) and how to implement them in Python / Julia.

GLRMs can recover many common dimensionality reduction (PCA, NNMF, etc); their goal is to create a numeric representation (X,Y) for tables (A) that simultaneously contain multiple types of data (boolean, categorical, numerical, ordinal, missing). We will discuss how to create such numerical representation, and how to use them.

Pre-requisites (ideal but not mandatory):

• Matrix multiplication ( A = XY )
• Understand what an objective function is ( think least squares)
• Basic python programming

Getting Started

Instructor

• Julio Cárdenas-Rodríguez Ph.D. Principal Data Scientist, United HealthCare, JCardenasRdz@uhc.com

Github repository

• https://github.com/JCardenasRdz/ResBaz-2021_Generalized_Low_Rank_Models

Packages needed for this workshop:

All the dependecies for this workshop can be installed via a yaml file (conda):

conda env create -f glrm_env.yaml
conda activate glrm_env

or a requirements file (pip)

pip install -r requirements.txt i

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