Applied Machine Learning with R

09:00-17:00, January 25 @ Foyer 6

Workshop / Overview

This workshop teaches you the basic principles of machine learning; how to use R to implement various machine learning algorithms, including Regression models, Decision Trees, and Random Forests; and how evaluate and optimize their performance for both regression and classification problems.

The workshop will consist of brief presentations followed by live demonstrations and hands-on exercises focusing on applied problems from medicine, education and business. The presented code and exercises will draw mostly on the excellent caret R package.

This workshop will be adapted from our typical 2-day course on ML with R. Feel free to explore our open materials on The R Bootcamp website, and specifically our previous R Bootcamp program.

To be checked before the workshop:
Workshop slides and materials
Workshop installation instructions
Workshop survey

Workshop / Outcome

Learn to implement the complete machine learning pipeline (pre-processing, fitting, tuning, evaluation) using R to solve applied regression and classification problems. 

Workshop / Difficulty

Beginner level

Workshop / Prerequisites

  • There are no strict knowledge prerequisites for this course
  • Prior experience with a programming language (e.g., R, Python), as well as basic knowledge of statistics, is helpful but not necessary. 
  • Own laptop with with R, Rstudio, and packages tidyverse and caret installed

Track / Co-organizers

Dirk Wulff

Founder, The R Bootcamp & Researcher, University of Basel

Markus Steiner

Instructor, The R Bootcamp

Michael Schulte-Mecklenbeck

Senior Lecturer University of Bern, Switzerland

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AMLD / Global partners