This workshop provides an overview of contemporary machine learning methods. We'll cover important terminology and popular methods so that you can determine whether machine learning is relevant to your research and what to learn more about if it is. This is a concept-focused, non-technical workshop. No laptops needed.

After this workshop, learners should be able to:

• Define the following terms: observation, feature, machine learning, supervised learning, unsupervised learning, regression, classification, clustering, training set, validation set, test set, cross-validation, overfitting, underfitting, model bias, model variance, bias-variance tradeoff, ensemble model;
• Explain the difference between supervised and unsupervised learning;
• Explain the difference between regression and classification;
• List and briefly describe popular machine learning methods;
• Give an example of an ensemble model;
• Explain what cross-validation is used for and give an overview of the procedure;
• Assess whether and which machine learning methods might be helpful for a given research problem.

Prerequisites

This workshop is designed for researchers from all domains and backgrounds. This workshop does not involve any coding.

Can't make it to this training? Check out upcoming workshop schedule. Recordings of prior similar workshops are also available in DataLab's training archive.