An approachable, flexible, and practical machine learning workshop for biologists

Abstract

The increasing prevalence and importance of machine learning in biological research has created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible, or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment, or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research. The ML4Bio workshop focuses on classification and was designed around 3 principles: (a) focusing on preparedness over fluency or expertise, (b) necessitating minimal coding and mathematical background, and (c) requiring low time investment. It incorporates active learning methods and custom open source software that allows participants to explore machine learning workflows. After multiple sessions to improve workshop design, we performed a study on 3 workshop sessions. Despite some confusion around identifying subtle methodological flaws in machine learning workflows, participants generally reported that the workshop met their goals, provided them with valuable skills and knowledge, and greatly increased their beliefs that they could engage in research that uses machine learning. ML4Bio is an educational tool for biological researchers, and its creation and evaluation provides valuable insight into tailoring educational resources for active researchers in different domains. The workshop materials are available from https://carpentries-incubator.github.io/ml4bio-workshop/ and the ml4bio software is available from https://github.com/gitter-lab/ml4bio.