Groupe d’études et de recherche en analyse des décisions

Introductory Machine Learning in R

Mouloud Belbahri Université de Montréal, Canada

Vahid Partovi Nia Chercheur, Département de mathématiques et de génie industriel, Polytechnique Montréal, Canada

Damoon Robatian Polytechnique Montréal, Canada

Logiciels requis


Vous devez vous inscrire afin de participer à l'atelier d'ici le 20 septembre.
Veuillez noter que le nombre de participant est limité et que le dîner ne sera pas fourni.
Si vous ne pouvez plus être présent à l'atelier, svp nous aviser afin de permettre à d'autres participants de pouvoir y assister.

This workshop has no pre-requisite. Having some statistical knowledge in the level of introductory statistics and elementary coding is beneficial.

This is an interactive one-day workshop. We will guide you step by step to get familiar with R focused on elementary machine learning. Bring your laptop with R and RStudio already installed.

R is an open-source statistical software, widely developed in academia and industry with more than 2 million users around the world. Many cutting edge companies such as google use R as their data analysis platform. The aim of this workshop is to introduce some elementary R skills such as data loading, data pre-processing, and data visualization. We will practice some machine learning libraries to execute several supervised, unsupervised, and semi-supervised learning algorithms.

Machine learning requires a wrap of several skills, such as coding, optimization, statistics, and data analysis. This set of skills facilitates extraction of knowledge from large volumes of structured or unstructured data. It is a subfield of artificial intelligence with the purpose of discovering the underlying pattern of data through predictive modeling. The ultimate goal is to adopt data preprocessing, statistics, and black box predictive algorithms in order to draw conclusions and take (automatic) actions from (large amount of) data.